# White Paper on Radio Channel Modeling and Prediction to Support Future
Environment-aware Wireless Communication Systems

Editors: Mate Boban and Vittorio Degli-Esposti

(September 2023)

COST CA20120 INTERACT
Working Group 1 (Radio Channels)

###### Contents

  1. 1 Introduction
    1. 1.1 Mandate of WG 1
      1. 1.1.1 Mandate of subWG 1.1
      2. 1.1.2 Mandate of subWG 1.2
    2. 1.2 White paper organization
  2. 2 Scenarios
  3. 3 Frequency bands of interest
  4. 4 Wireless channel propagation fundamentals
    1. 4.1 Basic propagation mechanisms
      1. 4.1.1  Waves in homogeneous media
      2. 4.1.2 Reflection, transmission, diffraction and scattering
    2. 4.2 Large-scale and small-scale propagation phenomena
      1. 4.2.1 Large scale propagation phenomena
      2. 4.2.2 Small-scale propagation phenomena
  5. 5 Channel sounder design & metrology
    1. 5.1 Channel Sounding Design
      1. 5.1.1 Wideband Channel Sounding
      2. 5.1.2 Antennas and Antenna Arrays for mmWave and (sub-)THz Channel Sounding
    2. 5.2 Metrology of Channel Sounding
    3. 5.3 Future Work
  6. 6 Channel measurements
    1. 6.1 sub-6 GHz Frequency Band
    2. 6.2 Mid-band (6 GHz - 24 GHz)
    3. 6.3 mmWave and (Sub-)THz band
    4. 6.4 Measurement data collection contributed to COST INTERACT WG1
  7. 7 Modeling methodologies
    1. 7.1 Stochastic models
    2. 7.2 Map-based models
    3. 7.3 Ray-based models
      1. 7.3.1 Challenges for mmWave & (sub)THz channel modeling
    4. 7.4 ML-based approaches
  8. 8 Channel parameter estimation
    1. 8.1 Parameter estimation techniques
      1. 8.1.1 Spectra-based techniques
      2. 8.1.2 Subspace-based techniques
      3. 8.1.3 Sparsity-recovery-based techniques
      4. 8.1.4 Maximum-likelihood-based techniques
      5. 8.1.5 Future challenges and directions
    2. 8.2 MPC Clustering
      1. 8.2.1 Shape-based clustering
      2. 8.2.2 Distance-based clustering
      3. 8.2.3 Density-based clustering
      4. 8.2.4 Computer vision-based clustering
      5. 8.2.5 Future Work
  9. 9 New technologies
    1. 9.1 Path Loss Measurements and Modeling for RIS
      1. 9.1.1 RIS channel modeling challenges
      2. 9.1.2 RIS channel models from literature
    2. 9.2 Channel Measurements and Modeling for ISAC
      1. 9.2.1 Sensing scenarios: monostatic, bistatic and distributed
      2. 9.2.2 Trends in ISAC channel modeling
      3. 9.2.3 Characterizing ISAC channels: recent measurements and modeling at mmWave bands
      4. 9.2.4 Open challenges in ISAC channel modeling
    3. 9.3 Channel Measurements and Modeling for ultra large arrays/MIMO
    4. 9.4 Application of ML and DL for Propagation Classification, Clustering and Regression
    5. 9.5 Data-Driven Radio Channel Prediction - Extrapolation in Frequency/Time-Space Domains and for Different Scenes and Systems
    6. 9.6 Hardware-in-the-Loop Radio Channel Emulation
    7. 9.7 Channel sensing using advanced antenna concepts for mmWave and beyond
  10. 10 Conclusions and future outlook

## Glossary

2D

    two dimensional
3D

    three dimensional
3GPP

    3rd generation partnership project
AI

    artificial intelligence
AP

    access point
AS

    angular spread
BS

    base station
CNN

    convolutional neural networks
CRLB

    Cramér-Rao Lower Bound
CSI

    channel state information
DKED

    double knife-edge diffraction
DL

    deep learning
DMC

    dense multipath component
DRT

    dynamic ray tracing
DS

    delay spread
DSS

    directional scanning scheme
DT

    decision tree
EADF

    Effective Aperture Distribution Function
ELAA

    extremely large antenna array
EM

    Expectation-Maximization
ESPRIT

    unitary Estimation of Signal Parameter via Rotational Invariance Techniques
FFT

    fast Fourier transform
GBDT

    gradient boosting decision tree
GSCM

    geometry-based stochastic radio channel model
IoE

    Internet of Everything
IoT

    internet of things
ISAC

    integrated sensing and communications
JSAC

    joint sensing and communications
LoS

    line-of-sight
MIMO

    multiple-input multiple-output
ML

    machine learning
MLP

    multi-layered perceptron
mmWave

    millimeter-wave
MPC

    multipath component
MUSIC

    MUltiple SIgnal Classification
OFDM

    orthogonal frequency division multiplexing
PDP

    power delay profile
PLE

    path loss exponent
PN

    pseudo-noise
PRBS

    pseudo-random binary sequences
RL

    ray launching
RIS

    reconfigurable intelligent surfaces
RL

    reinforcement learning
RMS

    root mean square
RT

    ray tracing
SAGE

    space-alternating generalized expectation–maximization
SL

    supervised learning
subWG

    sub-working group
THz

    terahertz
UE

    user equipment
UL

    unsupervised learning
WG

    working group

##  1 Introduction

by Mate Boban

COST INTERACT working group (WG) 1 aims at increasing the theoretical and
experimental understanding of radio propagation and channels in environments
of interest and at deriving models for design, simulation, planning and
operation of future wireless systems. Wide frequency ranges from sub-GHz to
terahertz (THz), potentially high mobility, diverse and highly cluttered
environments, dense networks, massive antenna systems, and the use of
intelligent surfaces, are some of the challenges for radio channel
measurements and modeling for next generation systems. As indicated in [1],
with increased number of use cases (e.g., those identified by one6G [2] and
shown in Fig. 1) to be supported and a larger number of frequency bands, a
paradigm shift in channel measurements and modeling will be required. To
address the particular challenges that come with such a paradigm shift, WG 1
started the work on relevant topics, ranging from channel sounder design,
metrology and measurement methodologies, measurements, modeling, and
systematic dataset collection and analysis.

Figure 1: Selected use cases that next generation wireless communications
systems aim to enable (based on [2]).

In addition to the core activities of WG 1, based on the strong interest of
the participants, two sub-working groups (subWGs) have been initiated as part
of WG 1: i) subWG 1.1 on millimeter-wave (mmWave) and THz sounding (subWG THz)
and ii) subWG 1.2 on propagation aspects related to reconfigurable intelligent
surfaces (RIS) (subWG RIS).

This white paper has two main goals: i) it summarizes the state-of-the-art in
radio channel measurement and modeling and the key challenges that the
scientific community will have to face over the next years to support the
development of 6G networks, as identified by WG 1 and its subWGs; and ii) it
charts the main directions for the work of WG 1 and subWGs for the remainder
of COST INTERACT duration (i.e., until October 2025).

In this white paper, particular attention has been devoted to the concept of
“environment awareness”, which is defined as the ability of communications
systems to sense in the broad context of object detection, positioning and
ranging, and even object imaging. The repercussions of environment awareness
are therefore discussed throughout the paper, ranging from the definition of
use cases and their requirements (Section 2), to required frequency bands
(Section 3), to the considerations on the design of channel measurements
(Section 6.4 and definition of channel models (Section 7), finally to
definition of new technologies such as integrated sensing and communications
(ISAC) (Section 9).

###  1.1 Mandate of WG 1

Figure 2: Topics addressed by WG 1 and its subWG s.

Extensive efforts are being devoted to obtaining a comprehensive understanding
of radio wave propagation in several frequency bands for the development of
future wireless networks. The task of WG 1 is to further this understanding by
providing an open and collaborative forum for the exchange of ideas,
definition of key challenges, and identification of directions for research on
radio channels. To that end, the efforts in WG 1 relate to propagation
modeling for radio systems, including the ones exploiting mmWave and higher
frequency bands (sub-THz and THz), where large contiguous bandwidths are still
available, and massive multiple-input multiple-output (MIMO) and beamforming
techniques, which will enable spectral-efficient connectivity in densely
populated areas. Understanding radio wave propagation has also been crucial to
new applications, including highly dynamic scenarios, internet of things (IoT)
and smart grids. Efforts on propagation modeling for these systems and
applications encompassed vehicular and mmWave cellular access [3], IoT, Smart
Grids [4], and energy efficient cellular radio planning. These propagation
modeling studies have been carried out using various measurement setups [5] or
combining measurements and theory for link- and system-level simulations [6],
addressing the time, angle and polarisation characteristics of multipath
channels, as well as the characterisation of material properties, outdoor-to-
indoor penetration loss, and link blockage [3]. Propagation models become
mature once supported by a vast amount of measured and simulated evidence of
radio channels, and by our understanding about them. Ultimately, such mature
propagation models may be able to perform real-time prediction of radio
environments and hence provide accurate-enough channel state information (CSI)
to aid radio communication systems and applications. As an example, a few
studies have addressed the real-time use of deterministic propagation models
to help estimate CSI [7], along with a location-aware CSI fingerprinting [8].
Their real-time use in localisation, beamforming, and resource allocation
algorithms is still in its infancy.

WG 1 is also committed to collecting data and sharing them to create large
reference sets for model development and training of machine learning (ML)
approaches, with WG 1 members already contributing several datasets for this
purpose (datasets available at https://interactca20120.org/wgs/datasets-2/).

Based on the discussion above, Figure 2 summarizes the main topics that WG 1
is addressing, along with the indication of specific topics that are handled
by the subWGs. WG 1 will contribute to each of these topics in order to reach
its ultimate goal – definition of a comprehensive channel modeling framework
that addresses new scenarios and frequency bands proposed for future wireless
communications systems.

####  1.1.1 Mandate of subWG 1.1

The goal of the subWG 1.1 (mmWave and THz sounding) is to concentrate the
expertise on radio channel measurements and analysis related to mm-wave
frequencies and up, which represent the frontier in experimental channel
characterization. Novel experimental set-ups, verification of channel
sounders, radio channel measurements in different environments/for different
applications are some of the key aspects to be investigated. The chain to be
covered ranges from the validation of the measurement equipment to the
analysis of the measurement results. The objectives of this subWG are to
extend the knowledge on propagation from empirical analysis and to develop
common practices, in order to create a rich pool of harmonized data from
diverse sets of measurements.

####  1.1.2 Mandate of subWG 1.2

The goal of the subWG 1.2 (RIS) is the analysis and modeling of propagation in
future smart radio environments empowered by controllable and smart surfaces.
These surfaces enable the manipulation of propagation characteristics,
including wavefront shape and polarization, and the minimization of signal
losses. This is particularly of interest at mmWave and higher frequencies to
extend otherwise limited communication ranges due to high path losses and the
very poor signal penetration into the shadow regions of objects and
obstructions. Taking the idea further, large numbers of smart surfaces would
enable smart environments where it is possible to optimize the channels and to
maximize the network throughput and efficiency.

In order to understand the benefits and limitations of radio channel
modulation with RIS, we have to properly understand the modeling and
performance of different types of RIS (e.g., reflect arrays, metasurfaces,
holographic surfaces, etc.). Modeling those properly is a key to continue to
the modeling and analysis of individual links, entire systems, and smart
environments. Therefore, the goal of this subWG is to extend the knowledge on
three fundamental aspects of RIS: 1) proper and realistic RIS models for
different RIS configurations and technologies, 2) propagation models for RIS
empowered links and systems, and 3) performance of smart links, systems, and
environments.

###  1.2 White paper organization

For each subsequent section of the white paper covering technical topics, we
attempted to cover the following: i) a brief description of the state of art,
including key references; ii) summary of COST INTERACT WG 1 contributions to
the topic111While COST INTERACT contributions are not made public, vast
majority of contributions are published in conferences, journals, or other
venues, either before or after submission to COST INTERACT. Throughout the
paper, we refer to those publicly available versions of contributions.; and
iii) identification of future work needed on the topic. By implementing this
approach, we hope the white paper serves as a reference for the researchers
looking to get a primer on channel measurements and modeling for future
communications systems.

The rest of the white paper is structured as follows. Section 2 describes the
relevant environments and channel modeling scenarios and Section 3 discusses
the frequency bands of interest. Section 4 provides an introduction into the
wireless channel propagation fundamentals. Relevant channel sounder designs
and metrology are covered in 5. Based on the identified environments and
frequency bands, Section 6 discusses the channel measurements. Channel
modeling methodologies are covered in Section 7, and Section 8 discusses
channel parameter estimation. Section 9 identifies new technologies and
techniques related to channel modeling that need to be implemented in order to
properly evaluate future communications systems. Finally, Section 10 provides
an outlook on the future work and concludes the white paper.

##  2 Scenarios

by Vittorio Degli Esposti

Early generations of wireless networks were conceived for a limited number of
propagation environments and use-cases. In terms of physical characteristics,
environments were classified into rural, urban and indoor [9], which
corresponded to an increasing attenuation and traffic density, and therefore
to a decreasing cell radius. Further classification into suburban and dense-
urban, or large-indoor, office and residential indoor is also widely used. The
most important use case was that of voice services and internet access
connectivity using mobile user equipment (UE) such as smart phones, tablets
and laptop computers. Over the years, driven by technology advances and market
demand, wireless networks have evolved into a multi-technology integrated
galaxy of systems, with a large variety of connected devices, scenarios and
propagation environments, as depicted in Fig. 1. Besides the traditional
scenarios described above, novel scenarios will include the use of new
frequencies in the THz and optical bands and of densified, cell-free networks
in high-traffic areas, the realization of the “Network of Everything” with
massive connectivity of objects and machines to realize “Smart Environments”
and “Smart Factories”, also with the use of ubiquitous Artificial Intelligence
and of Reconfigurable Intelligent Surfaces, the realization of three
dimensional (3D) networks including drones and UAVs as network components, and
finally the implementation of automated and connected cooperative driving
scenarios using dedicated or cellular-based networks and of Joint Sensing and
Communication techniques .

Although the concept of “environment” or “scenario” is a vague one that
encompasses physical – and therefore propagation – characteristics, frequency
band, technology solutions and applications, here we provide a brief overview
of the wide variety of scenarios that next-generation systems will likely have
to address, with reference to the classification in Fig. 1 and with particular
emphasis on COST INTERACT WG1 research.

##### Indoor and in-X high-performance-link scenarios.

It is well known that 6G-and-beyond systems will have to raise the bar of
achievable performance in term of transmission speed, throughput density, low
latency and reliability. This will require the use of higher frequency bands
in the mm-wave, THz and optical ranges, and of network densification [2, 10,
11]. The possibility of very high bitrates - of the order of Tbps - and very
low latencies of THz links will enable new application scenarios for indoor
and very short-range communications - also known as in-X communications - such
as high-definition holographic infotainment and ”teleportation”, ultra-
broadband mobile access for offices and public spaces, high-performance
wireless communication links for industrial applications, data centers, in-
vehicle inter-device and intra-device connections [11]. Visible Light
Communications (VLC) are envisioned especially for indoor environments, where
illumination LEDs, already strategically deployed across indoor premises, can
be conveniently reused for communication [12].

##### Vehicular scenarios.

Future transportation scenarios will be characterised by high mobility and
will involve cars, trains and unmanned aerial vehicles flying at low
altitudes. All of them will require massive use of radio applications
including Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I) and
Vehicle to Everything (V2X) connections as well as radar and ISAC schemes to
ensure cooperation, control and safety [13]. The dynamic nature of radio links
and networks in the transportation scenarios is the key feature to be
addressed in radio channel modeling research. Within the foreseen automated
and connected cooperative driving application scenarios, key assets are a
reliable wireless V2X connectivity and accurate localization. Most recent
vehicles and last-generation wireless systems users will have such
capabilities, while others will not (heterogeneous traffic). Environment-aware
and cooperative solutions will have to take advantage of connectivity,
localization and mapping information available to the former kind of road
users or to the edge cloud, to enforce safety for the latter and the whole
traffic system. Cooperative, Passive Coherent Radar solutions are being
proposed where signal emitted by the fixed infrastructure and vehicles
equipped with V2X can be reused as multi-static radar sources that the system
can opportunistically use to determine the location of vehicles and
pedestrians along streets and in proximity of road intersections [14].

#####  ISAC and environment awareness.

The CSI or at least information about multipath spatial characteristics should
be known at both radio-link ends to fully exploit the potential of massive
MIMO schemes as well as of highly directive mm-wave and THz links in mobile
environments. The problem will therefore have to be addressed, for example
through ISAC techniques, artificial intelligence (AI) techniques [15], or real
time use of digital-twin schemes with embedded propagation models [16, 7].
ISAC, especially at THz frequencies, will enable high-definition environment
”vision” applications, including environment mapping, medical imaging,
surveillance applications, safety enhancement applications in vehicular
applications, etc. All these methods, combined with the ubiquitous use of AI
can be thought as enablers of the so called “Environment Awareness” and
”intelligence” of future systems.

##### Smart and Reconfigurable Environments.

AI, universal wireless connectivity (Internet of Everything (IoE)) and RIS
technology will enable Smart and Reconfigurable Environments. For the first
time in the history of wireless systems, RIS and the use of Unmanned Aerial
Vehicles or drones (3D networks) will allow the customization of the
propagation environment with the purpose of optimizing performance and
enhancing the application potential [17, 18]. RIS allow the manipulation of
the reflected or transmitted wavefront, enabling interesting applications such
as anomalous (non-specular) reflection, focalization, signal processing.
Properly placed and configured RIS can be used to enhance mm-wave and THz
coverage, or to optimize channel capacity or system performance with a so-
called “Scatter MIMO ” approach [17, 18]. The combination of RIS and UAV is
also interesting in highly cluttered mm-wave and THz scenarios to compensate
for blocked line-of-sight (LoS) communication links and create controllable
and smart radio environments [18, 19].

##### Industrial Environment.

A particular candidate to become ”smart” is the Industrial Environment where
advanced wireless networks will allow a variety of disruptive applications.
Cable replacement with ultra reliable and high-performance wireless links is
very attractive for the great flexibility and increased reliability of
connections with sensors/actuators in moving parts and with robots. The use of
mm-wave and sub-THz frequencies will allow ultra low-latency (below 0.1 ms)
connections to avoid oscillating behaviours in control loops while enabling
high-definition environment sensing through ISAC to control the production
process and enforce safety [20, 21].

##  3 Frequency bands of interest

by Mate Boban

Spectrum is the main consideration for each generation of wireless
communication technology as more spectrum is needed to support higher data
rates [22]. Since mobile communication technologies evolve to new generations,
the use of spectrum continues to expand to higher frequency bands. The
spectrum expected to support environment-aware communications can be divided
into the following frequency bands.

  * •

Sub-6 GHz band: Virtually all of the spectrum up until 4G (LTE) has been
allocated in the sub-6 GHz band. This band continues to play a crucial role in
5G and is expected to be vital in 6G as well. This frequency band is the most
cost-effective option as a frequency range to guarantee wide coverage in
mobile communication systems.

  * •

Mid-band: The frequency bands between approximately 6-24 GHz are also
identified as competitive candidates for supporting the continued growth of
traffic and environment-aware applications, especially given the better
sensing performance of larger array size per unit area. To support the
continuous growth of traffic, at least 1 to 1.5 GHz of additional spectrum is
needed. The 5925-7215 MHz range has been identified as a potential candidate
to provide the needed spectrum. Moreover, compared to the sub-6GHz band, the
propagation attenuation of these bands increase in an acceptable range while
path loss will be combated by adopting advanced radio technologies, e.g.,
massive MIMO [23].

  * •

mmWave band: The mmWave band contains a relatively large amount of available
bandwidth, which is essential for ultra-high data rates and high-accuracy
sensing applications. However, operation in the mmWave band is more
challenging due to the unfavorable propagation characteristics compared to
lower frequency bands. In the 2015 world radiocommunication conference (WRC),
a variety of frequency ranges were proposed for IMT sharing study between 24
and 86 GHz and in WRC 2019; specifically a total of 17.25 GHz was identified
[24]. E-bands (71-76 and 81-86 GHz) are prime candidates to support larger
contiguous blocks in the future, mainly reserved for non-geostationary fixed-
satellite service systems (space-to-earth and earth-to-space) [25]. The upper
and lower 60 GHz, namely the 57-64 GHz and 64-71 GHz frequency ranges, further
provide large contiguous chunks of bandwidth to support device-to-device
communications through access and backhaul links and aeronautical and land
mobile services, respectively [24]. Furthermore, technologies such as
integrated access and backhaul (IAB) could make use of the available spectrum
available at the mmWave band [26].

  * •

Sub-THz and THz bands: Sub-THz and THz bands open new possibilities for
sensing and communication [27]. A total of over 100GHz in 92-275 GHz band is
allocated, whereas 130 GHz in 275-450 GHz band is identified for mobile
services or land mobile services [28]. At these frequencies, there are several
parts of contiguous spectrum exceeding 10 GHz, which makes it possible to
support very high data rates for short- and medium-distance communication. In
addition, THz bands bring enhanced sensing resolution thanks to the ultra-wide
bandwidth and shorter wavelength [29]. Further up the frequency, between 450
GHz up to 10 THz there is potential for further spectrum. While there exists
an unprecedented amount of spectrum in these bands, they also experience new
challenges [30]: extremely high transmission losses, molecular absorption that
creates non-monotonic pathloss over different frequencies, variability due to
weather conditions, effect of micro-mobility, etc. These effects need to be
addressed to ensure efficient use of the large spectrum. Additionally, the use
of visible light spectrum [31] has gained significant momentum in recent
years, given its ability to support novel communication and sensing use cases.

Figure 3: Spectrum bands.

##  4 Wireless channel propagation fundamentals

by Vittorio Degli Esposti, Conor Brennan, and Katsuyuki Haneda

###  4.1 Basic propagation mechanisms

While an exact description of radio propagation and therefore of a radio
channel might be theoretically possible via Maxwell’s equations, it would be
unwieldy and complex. Instead, it is possible to simplify propagation
description - while retaining relevant features and characteristics - in terms
of a set of basic mechanisms that will be reviewed in this section.

####  4.1.1  Waves in homogeneous media

The simplest case to consider is free-space, or propagation in vacuum. In this
case the power density decays inversely with the square of the distance,
essentially reflecting the fact that a constant amount of total source output
power is being spread over a larger total surface area as the wave propagates
further away from the transmitting antenna. For communication systems there is
an additional loss, proportional to the square of the frequency, due to the
frequency dependence of the effective aperture of a receiving antenna. At the
high mm-wave and THz frequencies of interest within COST Action CA20120 this
is a significant issue but can be mitigated by the use of narrowly focused
beams at transmitter and receiver. However this relies on the continued
existence of clear line of sight path. This is explored in [32] in which high
resolution dual-polarised double directional measurements are taken in the
context of industrial control communications operating at 300GHZ. Propagation
and blockage spatial and temporal characteristics are obtained from the
processed data indicating the presence of viable alternative communication
paths.

Propagation within any other homogeneous material that can be found in a radio
channel (e.g. water, air, building materials, human tissue) is broadly similar
in many respects to the free space case. The precise physical effect of a
particular material can be described with reference to its constitutive
parameters, namely its electric permittivity, magnetic permeability and
conductivity. These parameters capture the macroscopic effects of the
material’s atomic and molecular structure on any waves passing through them.
These effects manifest in several ways including a change in the wave’s phase
velocity (slowing, relative to the speed in vacuum) and also a change in the
characteristic impedance (the ratio of the amplitude of the electric and
magnetic fields) and the wavelength (the physical distance between successive
peaks or troughs along the wave). Importantly, the presence of conductivity or
dielectric hysteresis in so-called lossy media manifests itself as an extra
reduction in the power density as the wave propagates (in addition to the
spreading discussed previously). These effects are frequency-dependent which
leads to dispersion effects as the individual frequencies which comprise a
pulse travel at different speeds through the material, causing the pulse to
distort.

####  4.1.2 Reflection, transmission, diffraction and scattering

The primary complication afflicting radio propagation is the proliferation of
waves occurring at the boundaries between materials (such as when a wave
travelling in air strikes a wall). Referring to figure (4) reflection and
transmission occur when an incident electromagnetic wave strikes the face of
an object which is locally smooth on a scale comparable to the wavelength. In
such circumstances the incident wave produces a reflected wave travelling away
from the face and a transmitted wave propagating into the object, the
direction of propagation of both being governed by Snell’s laws of geometric
optics. The amount of power reflected from, and transmitted through, materials
is frequency dependant and their specification for new communication
frequencies at millimetre wave and higher is an important task being addressed
in COST Action CA20120. Several contributors have conducted studies of
reflection and transmission (penetration) loss for typical building materials,
concentrating on their variation with angle, frequency and polarisation. [33]
described the use of a wideband channel sounder to examine a variety of
materials at four discrete frequencies between 28 GHz and 70 GHz and noted the
increase of penetration loss with frequency. Continuous measurements of
reflection and transmission losses are made in [34] and [35] for 17 common
materials in the frequency range 2GHz to 170GHz. Oscillations in the
reflection loss as the frequency varies are noted, due to the effect of
internal reflections within the finite slab of material under test. These
diminish as the frequency rises, consistent with the increased penetration
loss as noted elsewhere.

Figure 4: Plane wave reflection from smooth boundary. Incident wave
${\vec{E}}^{i},{\vec{H}}^{i}$ produces a reflected wave
${\vec{E}}^{r},{\vec{H}}^{r}$ and a transmitted wave
${\vec{E}}^{t},{\vec{H}}^{t}$

Another research focus is on the development of so called reconfigurable
intelligent surfaces RIS which do not obey the above laws of geometric optics
but rather can reflect or transmit signals in preferred directions, thereby
improving coverage or reducing interference. Within COST Action CA20120
researchers are examining ways to accurately model them using commercial
Finite Element software [36]. This numerically intensive approach is shown to
give good agreement with the simpler Generalised Law of Reflection approach
but has the added advantage of being able to model scattering in all
directions and thus can be used for interference analysis. More details on
COST Action CA2 20120’s work on RIS are available in section 9.1

Diffraction occurs when a wave strikes the sharp boundary between two such
faces (such as at the edge of a building). In such instances the wave is
scattered in a continuum of directions, as defined by the so-called Keller
cone [37]. As the interface between regions becomes rougher (or equivalently
the frequency becomes higher such as is the case with mmWave and THz
communications) finer details at wavelength scales become important and these
simple well-defined mechanisms of reflection, transmission and diffraction
give way to the more general process of diffuse scattering, which as the name
suggests results in a proliferation of waves being scattered diffusely across
a wider angular range (as per figure (5). Surface scattering, where the
inhomogeneities are assumed to lie on the material boundary, has been studied
by participants in COST Action CA20120 with a variety of models proposed. [38]
and [39] both use numerically precise models based on the method of moments
with the former paper concentrating on efficient ways to solve the associated
computationally intensive equations while the latter contribution examined
depolarisation effects and the dependency on material, frequency and surface
size. The accuracy of a simple directional scattering (DS) model is assessed
in [40] and effective roughness and scattering coefficient are identified as
key parameters. The model is extended using a $t$ location-scale distribution
in order to make it better fit electromagnetic simulation results.

Figure 5: Laws of geometric optics gradually give way to diffuse scattering
(reduced specular lobe and increased side lobes) as surface roughness
increases

Several authors have identified the process of volume scattering, which occurs
when waves interact with fine inhomogeneities within a material, as being of
more importance than surface scattering. The Effective Roughness model is a
relatively simple tunable model and has been used to describe such scattering
at mm-wave frequencies [41]. In this contribution it was noted that materials
with complex internal structure (such as reinforcing mesh) can produce
significant backscattering resulting in a diffuse scattering model involving
two directional lobes (in the forward and backward directions). The effective
roughness model is heuristic but is based on a solid insight of the physical
processes that take place at a material boundary. In [42] the authors enhance
its scientific basis by modifying it so that it obeys the reciprocity
principle (i.e. is unchanged by an interchange of transmitter and receiver
locations). As seen, diffuse scattering models are often statistical or
heuristic in nature, given the uncertainty surrounding the physical form of
small scale inhomogeneities that produce them. This uncertainty is caused by
an unavoidable limitation in the level of detail of databases describing
buildings etc. Nonetheless some COST INTERACT contributors have examined what
can be achieved with an enhanced level of building geometric description. In
[43] the impact of building facade detail is studied by comparing ray tracing
output (see below) to measured data. It is concluded that the inclusion of
enhanced information about facade features (windows, ledges etc) can result in
a more discriminating identification of multipath components than would be the
case with simple diffuse scattering models applied to flat facades.

###  4.2 Large-scale and small-scale propagation phenomena

As discussed in Section 4.1, the quality of a radio channel is ultimately
determined by how electromagnetic waves interact with the materials within it.
Recognising that such a full electromagnetic description is practically
impossible, channel modeling has instead focused on describing the channel in
terms of a number of key parameters that have the greatest effect on the
performance of the digital communication scheme being enabled by the channel.
The range of parameters and the accuracy with which they need to be specified
have evolved in tandem with the communication schemes themselves as they grown
in sophistication over the decades. Parameters are divided into large scale
parameters and small scale parameters, depending on the range over which they
vary significantly compared to the wavelength.

####  4.2.1 Large scale propagation phenomena

Path loss
Path loss describes the large-scale reduction in power density as signals move
away from the transmitter. It is the most important parameter in channel
modeling since it ultimately determines the possible communications range and
its quality, i.e., the signal-to- noise ratio. It also determines the signal-
to-interference ratios (SINR) in the case of an interference-limited
environment within which frequencies are reused to ensure efficient use of
spectrum. From an energy-efficient networks perspective path loss defines the
minimum transmit power of base and mobile stations required to maintain a
target SINR and quality of service. Consequently, path loss is the most well-
studied parameter in channel modeling. Path loss is typically modeled using a
power law, wherein the loss is proportional to the distance raised to the
power of some specified exponent. Path loss exponents typically range from 1.5
to 4 for waveguiding to deep non-line-of-sight environments. Frequency
dependency of the path loss has also been studied thoroughly, indicating good
agreement with the theory, e.g., Friis’ law with constant-gain antennas. Even
though it is a subject that has been extensively studied, it remains a subject
of research when new spectrum, use cases and scenarios emerge, e.g., rural and
aircraft scenarios.
Referring to the log-log plot of Fig. 6, path loss is typically depicted as
the best linear fit extracted from the measurement data.

Figure 6: Path loss, shadow fading, and small scale/multipath fading.

Shadow fading
Path loss models assume a regular decay of power density with distance (e.g.
linear or piecewise linear when viewed on a log scale), In practice the power
density will display further random large-scale variations around this trend
as some locations suffer from a greater or lesser degree of obstruction by
buildings etc. This is referred to as shadow-fading and is typically modeled
using a suitable random distribution. In legacy below-6GHz cellular radio
systems, shadow fading is typically defined by fluctuation of signal strength
over different sectors of base stations (BSs) at the same communication
distance between base and mobile stations. A single BS site consists of
multiple BS units, each covering a limited azimuth and/or elevation angular
range called a sector. The level of obstruction in each sectoral direction
varies, with some possibly experiencing large blockages with a consequent
decrease of signal strength. The standard deviation of shadow fading is
therefore not necessarily identical to the second order moment of differences
between path loss and small-scale-averaged received signal strength normalized
to the transmit power. Still, such a definition of shadow fading is the only
one that can be used for radio communications systems and scenarios where the
use of sectors would not be popular, e.g., in indoor scenarios. An open
research question of shadow fading is its correlation over space and frequency
[44]. Referring to Fig. 6, shadow fading adds a slowly varying process on top
of the path loss.

Blockage
Blockage refers to the obstruction of the LoS path between a transmitter and a
receiver by an object (e.g., building, wall, human body, etc.). It can be
modeled either as part of the shadowing fading process or explicitly, where
the latter is preferable in case of a known/measured additional blockage loss
by a specific object [45, 46]. In case of explicit modeling, it is added as an
additional loss on top of path loss and shadow fading. Below we elaborate in
particular on human blockage, as it becomes increasingly relevant for handheld
or on-body user devices utilizing mmWave or higher frequency spectrum.

It has been shown through experiments that losses of a LoS connectivity due to
human blockage is reproduced well by modeling a human body as a set of
absorbing knife edges and considering diffraction on each edge, where the LoS
path is totally absorbed by the absorbing screen representing the body. The
use of absorbing knife edges is advantageous to model human body in its
simplicity because diffraction coefficients do not depend on polarization of
an incident wave, in contrast to the case of conducting screens. As the
analytical formula of diffraction coefficients assume infinitely long edges,
they are better applicable to higher radio frequencies where the human body
becomes electrically larger. A research question would be estimation of
blockage loss when there are multiple objects intervening the LoS connectivity
[47, 48]. The analytical treatment becomes much more complex than a single
body case because diffraction coefficients of an absorbing knife edge assume
incident plane wave, while an incident wave to the following edges after the
first one may not be a plane wave necessarily. The same problem encounters
when estimating extra losses to free-space due to multiple diffraction over
hills and buildings in long-range point-to-point links. In addition, as the
cross sectional area of the Fresnel zones becomes larger as longer connection
distance, estimation of the blockage losses are more challenging because only
a part of the Fresnel zones may be intervened by blocking objects.

####  4.2.2 Small-scale propagation phenomena

Large scale effects such as path loss and shadow fading describe variation of
power density that occur gradually on a scale of many wavelengths. In practice
it is noted that power density varies rapidly on the scale of the wavelength
also. This phenomenon, called _fast fading_ ,is caused by wave interference
between EM waves arriving at a given location via multiple paths of differing
lengths, i.e. by the propagation mechanism usually referred to as _multipath_.
Given the impossibility of precisely specifying these path lengths this is an
intrinsically random process and a detailed statistical modeling of such
small-scale fading effects is therefore an essential part of modern radio
channel models. Fast-fading signal-strength fluctuation are described using a
variety of statistical distributions, Raileigh and Rice distributions being
the most widely used for non-LoS and LoS channel conditions, respectively.
Multiple parameters are used to model time, frequency and space dispersion
effects of multipath propagation. These effects and are often represented by
Fourier transform pairs, e.g., space/angle, time/Doppler and delay/frequency,
the choice of which domain to use depending on the channel sounding and
modeling methods.

Channel models usually prescribe the second moments of the power spectrum in
the respective domains, i.e., angular, delay and Doppler spreads, or their
Fourier counterparts, i.e., spatial, frequency and time correlation intervals.
These parameters have been studied thoroughly through a wide range of channel
sounding from a few hundreds of Megahertz to sub-THz radio frequencies across
many important radio communications scenarios. However, it must be noted that
the sole spread or correlation parameter values do not suffice to reproduce
channels that resemble realistic conditions because many different shapes of
power spectrum or correlation functions yield the same parameter values.
Explicit knowledge, i.e., shapes of power profiles and correlation functions
is therefore usually accompanied with parameter values, as discussed in the
following.

Multipath dispersion and clustering
Power spectrum of multipaths over angles and delays typically does not have
equal magnitude over the domain, but shows some extents of power concentration
on specific angles and delays in each radio link. Such concentration of power
in the spectrum is represented by clusters in multipath channel modeling. Each
cluster can be defined by its concentration angles and delays, which are
called cluster centers, along with their distributions in the respective
domain. The latter is typically modeled by Laplace and exponential
distributions in the angular and delay domains. Setting the right cluster
centers and types of distributions allows us to reproduce the realistic power
spectrum shapes of a radio link realization while respecting their spread
parameter values. The use of clusters for multi-dimensional power spectrum
modeling of multipath channels is a well-established approach, as evidenced by
standard channel models that rely on them. An open research question is the
variation of angular and delay properties of clusters across different radio
frequencies, especially at mmWaves and higher frequencies. It has been
discussed among the wireless communication community that multipath channels
are sparser, i.e., the number of multipaths and/or clusters is smaller, at
higher radio frequencies. There are some indications from comparative channel
sounding performed at various radio frequencies [49, 50, 51, 52] that support
the conjecture, where the power spectrum becomes more “spiky”, i.e., dominated
by specular reflections while reduced scattering effects, so that clusters
become more distinguishable to each other. The clearer specular reflections
and reduced scattering are both explained by wave-interacting objects whose
surface roughness becomes comparable to the wavelength of the radio frequency,
making the wave-object interaction more angularly selective and the number of
multipaths arriving at the receive side smaller becomes less. The same reports
tend to show that, despite the sparsity, the spread parameter values do not
necessarily change noticeably. Some other reports, on the other hand, show
evidence from channel sounding at multiple frequencies that the power spectrum
shapes do not change noticeably across frequencies [53, 54]. The discussion of
multipath sparsity and their influence on spread parameter values is therefore
not conclusive yet, requiring further evidence from multiple-band channel
sounding in different scenarios. They have implications on cluster models
across the radio frequencies in standard channel models. Referring to Fig. 6,
multipath fading results in fast variations superposed on top of the path loss
and shadow fading.

##  5 Channel sounder design & metrology

  * by Diego Dupleich and Wei Fan

###  5.1 Channel Sounding Design

Channel sounding consists of “sounding” the environment with a known signal
and analysing the received echoes to characterize the propagation of
electromagnetic waves. Since both the transmitted and the received signal are
known, the time-variant channel impulse response can be extracted. The
complexity of the sounding set-up depends on the propagation parameters under
investigation and target channel models and systems. Nowadays, with mobile
wideband MIMO systems at high frequency in sight, the ultimate goal of
multidimensional channel sounding is to provide the necessary data to jointly
estimate the amplitude (polarization) and different geometrical properties of
the multi-path components in the channel: delay, direction of departure (DoD),
direction of arrival (DoA), and Doppler. In practice, the simultaneous
acquisition of these multipath component (MPC) properties is challenging and
there is frequently a trade-off between resolution in the delay domain
(bandwidth), angular domain (directivity), and Doppler (sampling rate).

####  5.1.1 Wideband Channel Sounding

Channel sounders are usually classified as frequency-domain or time-domain
channel sounders.

Vector network analyser (VNA) is a widely used type of frequency-domain
channel sounder, recording the frequency response between two ports of the
device, as shown in Fig. 7(b)a. They are popular due to the ease of
calibration, excellent dynamic range, scalable and flexible carrier frequency
and bandwidth settings. With the help of external frequency extenders, it
supports channel measurements in the mmWave/(sub-)THz bands as well. However,
there are some shortcomings, for example, slow measurement time (determined by
the number of swept frequency points and IF settings), short measurement
distance (due to high losses in the RF cable used to remote antennas), and
lack of VNA ports to support multi-antenna measurements. However, different
solutions have been investigated in INTERACT to work around some of these
limitations. Radio-over-fiber (RoF) has been implemented to solve the problem
of short measurement range for mmWave/(sub-)THz bands [55, 56]. Moreover, a
phase-compensation scheme that counteracts random phase variations has been
tested in [57, 58], achieving accurate and phase coherent measurements for
VNA-based directional scanning schemes (DSSs) and virtual-array schemes at
mmWave and (sub-)THz.

On the other hand, time-domain channel sounders utilize specially designed
wideband signals. Popular signals are multi-carriers and pseudo-noise (PN)/
pseudo-random binary sequencess (PRBSs) (displayed in Fig. 7(b)b), which have
special auto-correlation properties in time. In contrast to multi-carrier,
PRBS have the advantage of a low peak-to-average power ratio (PAPR), allowing
the optimization of transmit power, a scarce resource at high frequencies. On
the other hand, unlike with multi-carrier signals, the spectral power density
of PRBS is not uniform over the measured bandwidth, which also creates
difficulties on the frequency response calibration of the system. In
comparison to VNAs, time-domain channel sounders allow real-time measurements.
However, this requires expensive wideband digitizers that can be saved by
implementing sub-sampling or the sliding correlation architecture. This also
has the advantage of an increased SNR at the expense of larger measurement
times.

(a)

(a)

(b) Figure 7: Wideband channel sounding architecture (a) VNA-based and (b)
correlation based.

(b)

####  5.1.2 Antennas and Antenna Arrays for mmWave and (sub-)THz Channel
Sounding

For mmWave/(sub-)THz applications, it is of high importance to have knowledge
of the spatial profile of the channel. Several strategies have been reported
in the literature to capture this information, including the DSS and the
utilization of virtual and physical antenna arrays. In the DSS scheme, a
highly directional antenna can be centred on a turntable and rotated to record
the spatial profile of the channel. The DSS scheme has been the most popular
solution for mmWave and (sub-)THz bands because of its simplicity, low cost
and high-gain offered by the employed antenna. However, it is slow due to the
mechanical steering nature and its spatial resolution is essentially
constrained by the directivity of the antenna [55, 56]. The virtual array
solution is another popular scheme that employs a single antenna which is
sequentially moved in pre-defined spatial locations (i.e. virtual elements of
the array) [57, 58]. However, the DSS and virtual array schemes can only be
used for static propagation scenarios due to long measurement time. On the
other hand, time-varying scenarios can be measured with physical arrays. The
multiplexing antenna array scheme consists of several directional antennas
pointing to different spatial directions connected to a radio frequency (RF)
switch, enabling the direct recording of the spatial profile [59]. To decrease
the hardware complexity and cost while maintaining the performance of the full
digital structure, a switched array based channel sounder for mmWave bands was
reported in [60]. By activating different antenna pairs, channels of all
antenna pairs are measured at different time instants. Switched-array sounding
can accomplish one MIMO channel snapshot within a very short measurement time.
However, non-sequential antenna switching is needed to overcome the aliasing
in the parameter estimation of Doppler frequencies and angles of MPCs [61].
With phased arrays (i.e., with an analog beam-forming structure), the process
of beam-steering is much faster and it has been widely used in the mmWave
bands [62]. Though highly promising, its application to large-scale
configuration at mmWave/(sub-)THz bands has not been reported for channel
sounding. More flexibility and capabilities compared to phased arrays can be
achieved with a digital beam-forming structure, in which each antenna element
has an individual RF chain [59]. However, the number of RF channels is
essentially limited due to cost and complexity concerns.

###  5.2 Metrology of Channel Sounding

Metrological assessment of multidimensional channel sounding has gained
relevance in the latest years as it can be seen from different activities
around the world: the METERACOM project111https://www.meteracom.de, the NextG
Channel Model Alliance222https://www.nist.gov/ctl/nextg-channel-model-
alliance. (sponsored by NIST), and standardization activities in the IEEE
P2982 group (mmWave Channel Sounder Verification), which is working towards
recommendations of methods for verifying mmWave channel sounders performance.
These verification methods are usually based upon comparison of processed
channel measurements to either theory or to particularly designed artefacts
that generate MPCs with known characteristics in the different domains [63].
These artefacts can also be measured over-the-air [64], which offers the
versatility of including the measurement antennas and allowing mobility during
the test, enabling the verification of angle of arrival/departure and Doppler
[65].

###  5.3 Future Work

Within COST INTERACT WG 1 and subWG 1.1, we will be tackling the reduction of
measurement time in virtual-arrays and DSS schemes. It is important to build
statistically meaningful 6G channel models, which requires double directional
channel measurements at multiple locations for many deployment scenarios.
However, a key bottleneck for channel sounding at mmWave/(sub-)THz frequency
bands using the DSS and virtual arrays is the long measurement time associated
with the mechanical movement of the antenna. Virtual array based channel
sounder for the W-band (75 GHz - 110 GHz) was designed and employed for
channel measurements in [57, 58]. We will continue working on developing
virtual array channel sounder for the 220 GHz to 330 GHz frequency band. A few
works on channel sounder to support cell-free massive MIMO scenarios were
reported within COST INTERACT [66], which we will continue to cover. The
implementation of physical-arrays at (sub-)THz for dynamic measurements and
joint estimation of Doppler/angle/delay of MPCs will be addressed from a
metrological and practical point of view. Within the scope of ISAC, there is
plenty of space for the optimization of baseband excitation signals to
enhanced sounding performance, reduce uncertainties during measurements, and
optimally exploit the hardware resources. Non-linearities are also challenging
with the utilization of wideband multi-carrier signals and need to be
addressed from a metrological and practical point of view.

##  6 Channel measurements

  * by Mate Boban, Diego Dupleich, Wei Fan, Marco Skocaj, and Wenfei Yang

###  6.1 sub-6 GHz Frequency Band

The first four generations of cellular systems as well as IEEE 802.11 (WiFi)
standards up until a decade ago were all enabled by sub-6 GHz bands.
Therefore, over the last more than 30 years, a large number of measurement
campaigns have been carried out to characterize wireless propagation in sub-6
GHz bands [67].

However, these bands still garner interest, primarily due to emerging
application and deployment scenarios and use of novel antenna techniques. To
that end, recent studies presented at COST INTERACT meetings focused on the
characterization of high mobility scenarios, such as vehicular and indoor
factory environments, and considered new deployment approaches, such as cell-
free and massive MIMO architectures.

In [68], authors conducted channel measurements at 3.2 and 5.81 GHz in
vehicular propagation environments, including V2V, V2I, and V2P scenarios.
Together with channel measurements, the authors collected LiDAR data captured
by sensors installed in the connected vehicles and built a dataset with
coherent perception and propagation traces. In [69], authors performed
measurements in a cell-free vehicle-to-infrastructure communication scenario
using a real-time channel sounder operating at 5.89 GHz and with a bandwidth
of 80 MHz. At the transmitting side, they considered different setups with
different number of access points and antenna elements. At the receiving side,
multiple omnidirectional antennas were installed in a van and spaced more than
10 lambdas, enabling the measurement of dynamic and decorrelated channels. For
each setup, the authors evaluated SNR and root mean square (RMS) delay spread
(DS), and demonstrated that cell-free architecture can guarantee a better and
spatially more uniform link quality.

In [70], authors performed wideband and ultra-wide band measurements and
adopted a Bayesian approach to derive and empirical fading model for device-
free localization purposes. They considered different setups, including
outdoor measurements at 5.2 GHz and indoor measurements at 4 GHz. In [71],
authors carried out a measurement campaign to characterize indoor-to-outdoor
high-mobility propagation scenarios at 5.9 GHz. The transmitter antenna is
installed inside a building and placed on a rotating unit that rotates with a
constant velocity. The receiver is placed on the roof of another building at a
distance of 140 m. The results include normalized local scattering functions
for different velocities of the transmitted antenna.

In [72], authors considered a factory environment and conducted measurements
at 3.7 GHz considering two different deployment options: a co-located massive
MIMO antenna array and a unique randomly distributed array. Measurement
results are used to quantify the channel hardening effect observed when
increasing the number of antennas, and show that the usage of massive MIMO
arrays in rich scattering environment can reduce large scale power variations
and help in achieving more reliable wireless channels.

###  6.2 Mid-band (6 GHz - 24 GHz)

As the spectrum below 6 GHz is limited, current services are already using a
large proportion of the available spectrum. Therefore, resorting to higher
frequency ranges becomes a necessity. Frequency bands between 6 GHz - 24 GHz
are considered as a promising candidate for supporting future wireless
communications systems due to the lower path loss compared to higher (mmWave
and above) bands and a potential to support massive MIMO systems.

While up to now there have been limited contributions in terms of measurements
in mid-bands by COST INTERACT (e.g.,[73]), channel measurements for frequency
bands between 6 GHz and 24 GHz have been widely conducted in the literature,
where indoor scenarios [74, 75, 76, 77, 78, 79, 80] have driven more research
interest over outdoor scenarios [81, 82].

The study in [73] presented measurements for extremely large antenna array
(ELAA) with a 32×32 Rx virtual planar array in 10 GHz band in two indoor
environments (meeting room and classroom). Results indicate strong non-
stationary effects in the spatial domain as well as significant near-field
effects, both of which become non-negligible in ELAA channel modeling. As the
antenna arrays become larger and since massive MIMO systems are expected to
operate in mid-bands, further investigations of non-stationarity are needed
with ELAA. To that end, in [78], a 20×20 virtual uniform rectangular array
(URA) was used at Rx side in channel measurements at 13–17 GHz in a lecture
hall environment. Channel parameters, including channel gain, K-factor, and DS
were observed over the array which showed considerable variations without
deterministic trends. In [79], a 64×4 virtual URA was used at Tx side in
channel measurements at 11 GHz in a theater environment and channel parameters
such as shadow fading, DS, and coherence bandwidth were derived over the
array. Similar to [73], the spatial non-stationarity was again pronounced.

Primary channel parameters including path loss, shadowing, K-factor, DS,
angular spread (AS), cross-polarization ratio (XPR), and correlation
properties have been obtained in several environments. In [74], channel
measurements were conducted at 11 GHz and 14 GHz in indoor environments, where
the antenna elements were configured in a 7×7 square grid. The path loss
exponent (PLE) and DS were derived from the averaged power delay profile (PDP)
to remove the effect of small-scale fading. [75] presented a complete
parametrization for a three-dimensional (3-D) geometry-based stochastic radio
channel model (GSCM) at 10.1 GHz based on a measurement campaign in a lobby
environment. To obtain the 3-D spatial information, a virtual conformal array
consisting of four 9×9 planar arrays was used in the measurements. The
estimation of signal parameters via rotation invariance techniques (e.g.,
ESPRIT) were employed to estimate the MPC. MPCs were first clustered by the
“Power K-means” algorithm and then cluster parameters were also obtained. The
study in [81] carried out similar parametrization procedure based on channel
measurement at 11 GHz in a micro-cell environment. A virtual uniform circular
array (UCA) with 12 elements were used in measurements, hence the angular
spread was only available for the azimuth plane. In [77], measurement
campaigns were conducted by the DSS at 13–17 GHz in an indoor lecture hall and
a laboratory environments with high gain horn antennas. The space-alternating
generalized expectation–maximization (SAGE) algorithm was applied to de-embed
the effect of antenna response and then the DS and AS were obtained.

The characteristics of specific propagation mechanisms, such as diffuse
scattering and diffraction, have been also studied for the mid-band. [76]
characterized diffuse scattering based on MIMO channel measurements at 11 GHz
in indoor environments. Propagation parameters of diffuse scattering were
jointly estimated by the RiMAX-based estimator, where incoherent plane waves
due to diffuse scattering is modeled stochastically as dense multipath
component (DMC). The measurement results showed that significant DMC exist,
which affected the eigenvalue structure of the MIMO channel.

In summary, existing channel measurement campaigns in mid-band have been
conducted in various indoor but few outdoor environments. The measurement
results covered statistical channel parametrization, non-stationarity analysis
for MIMO channels, and propagation mechanisms study.

Future measurement campaigns in mid-band are expected to cover more
propagation scenarios, especially outdoors, which are necessary for defining
proper channel models for outdoor scenarios in these bands. Further
measurements and analysis are required to explore characteristics of massive
MIMO channels, including how the propagation environment affects massive MIMO
system performance in mid-band.

###  6.3 mmWave and (Sub-)THz band

The characterization of propagation from measurements at mmWave and the lower
THz bands has gained a lot of attention in the recent years. The free blocks
of spectrum available in these frequencies enable the implementation of high
data rate wireless links with enhanced capacity and with an unprecedented
level of resolution that makes them suitable for sensing applications.

However, there are propagation aspects related to the wavelength at mmWave and
(sub-)THz differing to the well known and studied sub-6 GHz bands. As the
frequency goes up, transmission loss will become high, diffraction becomes
much weaker, and penetration becomes very difficult, making the propagation
channel much sparse and specular. In addition, in the mmWave and (sub-)THz
range, particles in the atmosphere requires further considerations for some
frequency bands.

To compensate the increased isotropical path-loss, high-gain radio interfaces
need to be employed. Therefore, the spatial characteristics of the channel
become more relevant for channel modelling and system design, since the
information on where to point the antenna beam is of special importance.
Consequently, even empirical path-loss models have also been adapted from the
typical isotropical characteristics of the antennas to consider the
directivity [83].

Therefore, there is paradigm shifting from purely stochastic towards hybrid
stochastic/deterministic models for these frequency bands with the inclusion
of deterministic components. This requires a more precise characterization of
the electromagnetic properties of different constructive materials and a
deeper analysis on the scattering properties. The absorption coefficient and
refractive index of typical building materials (glass, plaster, and wood) has
shown a good agreement between the measurements and the results of the Fresnel
equations, [84]. On the other hand, diffuse scattering is modelled by
extending Fresnel equations for specular reflections with a Rayleigh factor
obtained from the knowledge of statistics of surface roughness by Kirchhoff
theory of scattering [85].

COST INTERACT contributions on the topic of material penetration and
reflection losses in a wide range of frequencies covering from sub-6 GHz to
the lower THz bands (up to 170 GHz) have shown a relative low dependence on
frequency of the reflection coefficients for the majority of the materials
under test, but an increased penetration loss above 100 GHz [86, 87]. In
addition, measurement-based analysis from different typical construction
materials at 27 GHz showed that the scattering from internal structures can be
relevant, especially in the case of Gypsum-board dividing-walls which have a
low penetration loss and relevant internal in-homogeneity [88].

Regarding weather and influence of rain in point-to-point mmWave links, the
attenuation to long time exposure to rain in direct and side (NLOS) links at
25.84 GHz and 77.52 GHz have been measured and modelled in [89], showing a
higher attenuation on the side links due to the scattering from the rain.

Human blockage also becomes more severe because of the directivity of the
radio interfaces and the increased penetration losses with frequency.
Frequently, two different approaches are used on the characterization of human
blockage: an empirical, based on the analysis of fading statistics, and an
analytical, based on the double knife-edge diffraction (DKED) model. The time
variant human shadowing statistics at 60 GHz inside an Airbus 340 have been
studied using different antenna types in [90]. Similarly, short-range
measurements at 60 GHz in [91] show that the signal level attenuation follows
a Gaussian distribution. In [92], 73.5 GHz measurements in pedestrian crowd
scenarios have been used to derive a model based on Markov’s chain, which is
also considered under the stochastic modelling approach in the 3GPP TR 38.901.
On the other hand, analytical models based on the knife-edge diffraction
principle predicts the obstruction loss based on the Huygens’ principle: the
diffracted front waves interfere constructively and destructively behind the
blocker. Human blockage measurements in several mmWave bands up to 60 GHz have
been compared to different modelling approaches in [93, 94], where a clear
frequency dependence on the losses has been observed, with maximum losses up
to 25 dB at 60 GHz. Similar results at 70 GHz are presented in [95], where
measurements are compared to the predictions with the DKED model considering
the directivity of the antennas. COST INTERACT contributions in human blockage
at (sub-)THz have been analysed from measurements in [96]. People with
different complexion have been set frontally and laterally interrupting highly
directional links, showing attenuation results higher than 25 dB and 30 dB,
respectively. These results are underestimated with theoretical diffraction
models as the METIS and DKED, which need to be extended including the antenna
directivity, as discussed in [95].

Regarding the characterization of propagation at (sub-)THz, there are already
several measurement campaigns in different scenarios, covering from very short
link applications as desktop [97], rack‐to‐rack communications for server
rooms [98]; to middle-range applications as wireless links in meeting room
[99], shopping mall and airport [100], between others; and outdoor
applications as parking lot [101], courtyard and crossroad [102], train
stations [103], etc. The impact of polarization in the channel when
considering highly directive radio-interfaces at (sub-)THz has been studied in
controlled experimental set-ups and in real complex environments in [99],
where the dependence of the path gain on the incident and reflected angles is
observed and contrasted with Fresnel. In recent times, (sub-)THz has also been
targeted to industrial applications [104], and therefore the characterization
of propagation in this environments has gained a lot of relevance, [105]. COST
INTERACT contributions in (sub-)THz measurements in industrial settings and
machines have been presented in [106]. The temporal/spatial characteristics of
the channel in an external access point (AP) to inside of machine (through
penetration by protective glass) shows a channel with a rich set of multi-path
components from the different metallic frames and machine components. A very
precise idea of the location of the different objects in the environment can
be depicted from the geometrical properties of these MPCs. This offers an
immense playground for the implementation of sensing applications that can be
used to control different production processes. The polarization of the MPCs
also showed a behavior in concordance with the Fresnel equations. In addition,
the obstruction of the LOS component by different parts of the machine or
external items as a forklift truck passing by showed that there are remaining
MPCs that can be used to maintain a considerable link budget for
communications.

Figure 8: Power azimuth/elevation profile at RX in an external AP to inside of
machine scenario at 300 GHz, [106].

However, despite the differences in propagation between the sub-6 GHz and
higher bands, simultaneous multi-band measurements at different frequencies in
several environments have yielded striking results in similarities: the
dominant MPCs are mostly present in the different bands [107, 108, 109, 110].
Hence, there is a high correlation between bands, which has been further
discussed and included in channel models [111].

COST INTERACT contributions in simultaneous multi-band measurements at sub-6
GHz, mmWave, and (sub-)THz in different scenarios have shown strong
similarities on the multi-path components in the different bands [110, 112,
113]. The dominant paths are present with similar gain and, different with was
expected, instead of an extremely sparse channel, dense-multi-path components
have also been observed at mmWave frequencies in many different scenarios. The
PDPs and Doppler spectral densities (DSDs) from multi-band (3.2 GHz and 34
GHz) V2V measurements in urban street scenarios showing strong similarities
are displayed in Fig. 9, [112]. This similar distribution of the MPCs
motivates to further explore the use of sub-6 GHz channel properties for beam-
forming at mmWave: the analysis of the multi-band measurements in an
industrial scenario from [110] shows that this can be effectively exploited in
NLOS, [114]. Similar results on the distribution of MPCs in different bands
have been observed in multi-band mmWave and THz measurement in a conference
room in [113], where multiple common scatterers and the presence of high order
reflections at THz have been observed.

Figure 9: Power delay profiles and Doppler spectrum densities at 3.2 GHz and
34 GHz in V2V scenarios, [112].

Future measurements need to be designed to cope with the requirements on
channel models to cover the spectrum from sub-6 GHz to THz and consider the
needs of ISAC applications. However, in case of the (sub-)THz bands,
measurements are still rudimentary due to the complexity of the channel
sounders and set‐ups required to capture the multi‐dimensional properties of
the channel, making it often impossible to resolve different dimensions
simultaneously. Therefore, significant work needs to be spent on the joint
estimation of Doppler, angular, and delay structure of the MPCs at higher
frequencies.

###  6.4 Measurement data collection contributed to COST INTERACT WG1

Empirical measurements are fundamental for the design, understanding and
validation of radio channel models. Furthermore, the creation and sharing of
standard reference datasets enables data-driven and hybrid model design
approaches in an effective and reproducible way. COST INTERACT’s open
collaborative environment constitutes a unique opportunity to share
measurements, simulation scenarios and models inside and outside the action.
In this regard, the Horizontal Activity group 1 (HA1) is responsible for
datasets’ setup and maintenance. A total of four datasets, which are briefly
reported and described in the following and made available at
https://interactca20120.org/wgs/datasets-2/, have been collected by individual
institutions and publicly shared to support the research activities and
scientific collaboration within WG1. While we briefly describe the up-to-now
contributed datasets below, WG1 remains open to further contributions, which
will be made available at the same website.

  1. 1.

Indoor high-speed channel sounding at sub-6GHz and mmWave [115]

Vienna University of Technology conducted measurements to compare sub-6GHz
(2.55, 5.9 GHz) and mmWave (25.5 GHz) indoor wireless channels in a high-speed
scenario. For all measured scenarios, the wireless channel is measured with
the same transmitter and receiver antenna positions, but with different center
frequencies and velocities. This allows a direct comparison of the measured
wireless channel in terms of fading environment and channel statistics.
Results are provided in terms of time-variant channel transfer functions for
discrete-time (snapshots) and frequency (subcarriers).

  2. 2.

UPCT Indoor 5G measurements at 1-40 GHz [116]
UPCT conducted indoor LoS MIMO measurements in the frequency range from 1 GHz
to 40 GHz. Using a Vector Network Analyzer (VNA) setup, characteristic
parameters including the relative received power, path loss, root mean square
DS, and K-factor were extracted and compared with ray-tracing simulations.

  3. 3.

Measured dataset for performance analysis of wireless systems in real-world 60
GHz indoor channels [117, 118]
This dataset employs channel measurements of an indoor office environment at
60GHz from the measurement campaign held in Brno University of Technology
(BUT), at the Department of Radio Electronics (DREL). The following
measurements are employed [117] for the characterization of an indoor channel
model for an IEEE 802.11ad single carrier physical layer (SC-PHY) MATLAB-based
simulator, which is also provided.

  4. 4.

Transmitter Identification and Fingerprinting based on RF Imperfections [119,
120]
These measurements are a compilation of the results [119] of experiments run
on a series of datasets gathered in the Future IoT/Cognitive Radio testbed
[120]. Such measurements can be employed for the detection of hardware
imperfections in RF transmitters in order to identify a specific transmitter
among others.

##  7 Modeling methodologies

  * by Tommaso Zugno, Enrico Vitucci, Nicola di Cicco, Diego Dupleich, Wei Fan, Ke Guan, Danping He, and Andrej Hrovat

In the following sections, we present different channel modeling methodologies
that have been proposed in the literature, including stochastic, map-based,
ray-based, and ML-based approaches. In each section, we describe the main
concepts, overview the state of art, and summarize recent developments.

###  7.1 Stochastic models

The characterization of wireless channels is of paramount importance for the
design and evaluation of wireless systems, however, a deterministic knowledge
of the channel behavior is difficult and impractical to obtain, as it requires
to perform measurement campaigns or to run complex simulations. For this
reason, a common approach is to adopt models which represent wireless channels
as a stochastic process whose properties resemble real propagation phenomena.
In this regard, GSCM is one of the most widely adopted class of stochastic
channel models. GSCMs adopt a stochastic approach to account for the presence
of scatterers in the environment, and apply geometric properties to model
signal propagation through multiple paths. The main advantage of this method
is the possibility to easily represent different scattering environments by
changing the parameters and/or probability distributions that model the
physical properties of scatterers.

Figure 10: Geometry-based stochastic channel model.

As represented in Fig. 10, signal propagation between a transmitting and a
receiving node is modeled by the superposition of multi-path components
(MPCs), each representing a plane wave travelling along a different path. MPCs
depart from the transmitting node with different angles, and arrive at the
receiving node from different directions, with different amplitudes, delays,
and polarizations. Typically, MPCs that exhibit similar characteristics are
grouped together into clusters. Characteristics of each cluster (e.g., power,
delay, AoA/AoD, polarization, etc.) are derived from random variables whose
distribution depends on the scenario under consideration.

Over the years, several works proposed GSCMs able to represent different
scenarios and use cases, such as 3rd generation partnership project (3GPP) TR
38.901 [111] (depicted in Fig. 11), COST 259 [121], Winner II [122], and
Quadriga [123]. In particular, 3GPP TR 38.901 was selected as the reference
model for the evaluation of cellular systems by the standardization community.
This models supports a wide frequency, between 0.5 and 100 GHz, and the
modeling of different propagation scenarios, including urban, rural, and
indoor scenarios, within the same framework. Despite offering a high
scalability and good generalization properties, it presents inherent
limitations related to its fully-stochastic nature. For example, it does not
provide a good support for the modeling of channel dynamics and inter-link
correlation, thus preventing spatially-consistent evaluations. These issues
can be overcome by adopting other approaches, such as the one described in
[124], albeit at the cost of increased complexity and reduced number of
applicable scenarios [1].

As part of the COST INTERACT action, GSCM models have been used for the
performance evaluation of emerging use cases, such as vehicular and rail
communications. In [125], authors used a GSCM to build a digital twin for
assessing the reliability of vehicular communications, while in [126], authors
used a GSCM to perform spatially-consistent real time simulation V2X
scenarios. Moreover, in [127] authors used a GSCM to train a frame error rate
prediction algorithm for wireless communications among vehicles. In [128] and
[129], authors designed and validated a new model for train-to-train
communications.

Figure 11: Block diagram of the 3GPP GSCM model, from [111].

Other works proposed new approaches to overcome the limitations of current
GSCM models for the simulation of next-generation wireless systems. Indeed,
the introduction of novel paradigms, such as integrated sensing and
communications, reflecting intelligent surfaces, and ultra-massive MIMO,
require fundamental changes in the way the channel is modeled. For example, in
[130] authors extended the 3GPP TR 38.901 framework [111] for the joint
modeling of communication and sensing channels. They introduce the concept of
sensing clusters and describe the additional steps for the generation of the
corresponding parameters. In addition to delays, powers, and angles, rays in
sensing clusters are characterized by echo angles and radar cross section.
Pathloss and shadowing are applied individually for each sensing cluster, and
a multi-bounce model is applied to map scatterers to geometric positions. In
[131], authors proposed a GSCM for RIS-assisted communications which accounts
for movements of terminals and clusters, and the time evolution of clusters in
space. The channel impulse response is expressed as a summation of the direct
path between BS and UT, and the indirect path reflected by the RIS. The
reflecting properties of the RIS are modeled through the $\Phi$ matrix which
includes the phase responses of the reflecting elements. A birth-death process
is used to simulate the evolution of clusters in the space domain. In [132],
authors developed a 3D geometry-based double-spherical model for ultra-massive
MIMO communications at THz frequencies which accounts for the nano-material
properties of plasmonic-based arrays.

###  7.2 Map-based models

One of the issues regarding the GSCM approach is the modeling of spatial
consistency among different links. To solve this issue, other approaches
applying ray tracing (RT) principles based on simplified maps of the
environment have been proposed [133].

An example of such approach is described in [134]. This model generates the
channel response following the step procedure represented in Fig. 12. The
first step consists of creating the map, including the definition of
transmitter and receiver positions, and the placement of blockers and
scatterers. Then, a simplified RT algorithm is used to compute the propagation
paths and determine delays, departure and arrival angles. Finally, the channel
impulse response is obtained by modeling the interaction of paths with
blockers and scatterers according to the main propagation phenomena, including
LoS propagation, reflection, diffraction, scattering, shadowing, and antenna
patterns.

Figure 12: Block diagram of the 3GPP map-based model, from [111].

Other models based on the same principle are available, the most popular being
the 3GPP map-based model [111] and the NYUSIM model [135]. More recently, the
authors in [136] proposed a map-based channel model for UAV communications at
mmWaves, while [137] presented a model for the evaluation of integrated
sensing and communications. Moreover, a novel site-level deterministic model
adopting a grid-based approach was presented as part of the COST INTERACT
action [138].

###  7.3 Ray-based models

Ray-based approaches relies on the high frequency approximation, where
electromagnetic waves are modeled as rays, following the principles of
geometric optics for reflection and transmission. Two main approaches can be
identified: (i) image ray tracing (RT) and (ii) ray launching (RL). Ray
tracing methods compute all rays that can reach the receiving point, e.g., by
means of the Image Method represented in Fig. 13(a), then apply attenuation
factors to each ray to account for the propagation phenomena. The main
drawback of this approach is the computation time, which increases
exponentially with the number of interactions. In ray launching methods,
multiple rays are launched from the transmitter into different directions
according to a proper angular discretization, as depicted in Fig. 13(b). As in
RT, the field propagated by each ray is computed by taking into account all
basic interaction mechanisms. Rays that reach the receiver contribute to the
overall channel response, while those that miss the receiver or become too
weak are dropped and not propagated further. In this case, the computation
time increases with the number of launched rays; therefore, this parameter
controls the trade-off between accuracy and computation time.

(a) Ray tracing.

(b) Ray launching.

When applied to highly dynamic scenarios, both methods still suffer from the
problem of high computational complexity that prevents their use in real-time.
One example is the case of vehicles moving at high speed and
transmitting/receiving or generating scattering in a dense urban environment.
Implementations on game engines like the one proposed in [126], solved this
problem by adopting GPU parallelization. Other emerging paradigms like dynamic
ray tracing (DRT) can then be applied to mitigate complexity growth [139].
Since the multipath structure remains essentially the same within a given time
interval $T_{C}$, it is possible to predict the multipath evolution on the
base of the current multipath geometry, assuming constant speeds and/or
accelerations for moving objects within $T_{C}$, and using analytical
extrapolation formulas. This is done without re-running a full RT for every
”snapshot” of the environment, therefore providing substantial savings in
terms of computation time. The DRT is then a helpful tool for decreasing
computational complexity and accelerating channel calculation, but also its
use in real-time becomes possible. When DRT is embedded in a mobile radio
system and used in real-time, ahead-of-time (or anticipative) channel
prediction is possible, thus opening the way to interesting applications. The
DRT method is flexible enough to be employed either in a fully deterministic
case, or when the path geometry is derived through statistical realizations of
the environment, according to the GSCM approach. Therefore, a natural way of
future development of highly dynamic channel modeling lies in further
optimization of the execution time via merging the DRT approach with the game
engine-based GSCM framework.

Another recent development is the integration of RIS models into RT/ray
launching algorithms in order to carry out realistic RF-coverage evaluations
in RIS-enabled communication scenarios. In [140], a previously developed RIS
macroscopic model based on a ”Huygens-like” approach [141] has been embedded
in a RT tool, and the performance of the RIS-based solution has been analyzed
in simple reference scenarios by modifying a few simple parameters of the
model. The results show that a gain of about 15–20 dB can be obtained in
blind-spot locations with proper RIS placement and configuration, without the
use of any additional active radio head, even when using simple designs such
as pre-configured lossy phase-gradient metasurfaces.

Future studies within the COST INTERACT action will deal with the development
of a fully ray-based RIS model, which allows for better integration into RT
algorithms and more realistic predictions involving multiple-bounce
interactions where the RIS can be in any place in the interaction chain.

####  7.3.1 Challenges for mmWave & (sub)THz channel modeling

For any RT simulator, every object which exists within each scenario resembles
kind of a Lego “building block”. Once all the building blocks are realized,
any communication scenario can be implemented. With the available propagation
models, the full-dimensional channel information can be obtained by employing
the RT methodology [142]. Nevertheless, the actual implementation of RT
techniques within the THz band still faces significant challenges which need
to be considered for future research [143]. Some of these challenges will be
presented next as open research problems.

Ultra-massive MIMO (UM-MIMO) systems generate very narrow beams to compensate
for the very high path losses encountered in the THz band. Since the RT
experiments are site-specific, the correlation of all the sub-channels created
by UM-MIMO antennas can be characterized by RT. However, with possibly several
hundreds of antennas, the computational and storage capacities for such
systems will dramatically increase [144]. One possible solution to this
problem is to develop a cloud-based RT with high computation and storage
capabilities.

Antenna beam management is another important challenge which needs to be
carefully considered. Its main function is to steer the antenna towards the
strongest ray/path and thus supporting the mobility of UE. The current
assumption for 5G mmWave is that the antenna beam from the BSs sweeps all
possible directions every 5 ms, while the UEs will transmit a short message as
its response. However, since the THz beam will become much narrower, the time
required to check all possible directions will significantly increase. With
the aid of RT, firstly, the omnidirectional antenna can be used to directly
identify the strongest path. Then, the BS or/and UE can select their own beam
following the RT simulation results in advance.

Complex multipath is another challenge caused by the “multi-structures”
configuration. One solution for identifying the characteristics of the complex
multi-paths is to integrate the individual transfer functions of the
propagation graph with the aid of RT. This is a new hybrid channel modeling
approach which is based upon the join processing of RT and graph theory. It is
our belief that such hybrid approach is a promising approach for the accurate
and efficient channel modeling of such dense MPCs.

###  7.4 ML-based approaches

Electromagnetic propagation is a complex phenomenon, as it depends on
multiple, different factors, including the properties of the propagating
signal (e.g., intensity, frequency, bandwidth, polarization, etc.), the
geometrical and electromagnetic characteristics of the environment, and the
specific position of the transmitter and the receiver inside the environment.
As ML methods are inherently fit to take care of complex problems [145], their
use for wireless channel characterization has been attracting increasing
attention over the last years [146], [147], [148].

If physical insight is heavily limited by the complexity of the target
problem, a ML-based propagation model basically consists of a black box, that
provides the existing, underlying pattern between some input data or between
some output labels and the corresponding input features without any clear
explanation. Conversely, ML can be aimed at improving the accuracy of some
baseline propagation model through the introduction of effective correction
factors [148] in case some physical/theoretical insight can be inferred.

To what extent a ML propagation tool can reliably mimic the electromagnetic
propagation process depends on the accuracy of the training stage, where a
large amount of propagation data are effectively fed to the tool in order to
learn the way the wireless channel actually behaves. Training therefore
represents a crucial task that must be carefully planned and carried out.
Propagation prediction through a well trained ML tool is expected to be
accurate and fast at the same time, to the extent that it can be relied on
offline, i.e., for the design of wireless networks and systems, but also in
real time, i.e., to assist the system (either end users or network equipment)
during working operations.

We now provide a concise overview of the main learning paradigms in ML,
contextualized by some illustrative applications in propagation. We then
elaborate on practical guidelines for choosing the most appropriate ML
algorithm for a given task, and we provide relevant pointers for future
research directions of interest. The fundamental learning paradigms in ML are
supervised learning (SL), unsupervised learning (UL), and reinforcement
learning (RL). In SL, the task is to learn an input-output mapping given a
finite dataset of input-output examples. SL problems include, but are not
limited to, regression, classification, forecasting, ranking, and
segmentation. Examples of SL problems in propagation are Path Loss regression
[149] and LoS prediction [150]. In UL, the task consists of learning patterns
and/or extracting useful information from data. UL problems may include
clustering, dimensionality reduction, feature selection, density estimation,
representation learning, and synthetic data generation. Finally, in RL, the
task consists of learning a policy that, after repeated interactions with a
dynamic system (or environment), maximizes the long-term accumulation of
reward signals. RL is commonly applied to optimization and control tasks for
which SL, i.e., learning to imitate an optimal control policy, is unfeasible
(e.g., due to the problem of deriving an optimal policy being intractable).
Relevant examples in propagation include antenna tilt control [151] and
coordinated beamforming [152].

After identifying the learning paradigm that is most pertinent for a given
task, the next crucial design point is the choice of the algorithm. In this
regard, the “No Free Lunch” theorem states that all ML algorithms are “equally
bad” if averaged over all possible optimization tasks [153]. While this result
may sound discomforting, it tells us that the choice of an ML algorithm is
mainly driven by the structure of the data at hand, i.e., one should choose an
algorithm that can be proven (either theoretically or empirically) to be most
efficient for the structure of the input data. Broadly, we can discriminate
between tabular (or structured) data and unstructured data. As the name
implies, tabular datasets consist of data that can be structured as a table,
such that each row represents a single observation, and each column represents
a distinct feature. For SL problems, empirically, the best-performing models
for tabular data are ensembles of decision trees (DTs), such as Random Forests
and, more prominently, gradient boosting decision tree (GBDT) models [154].
GBDT models achieve a remarkable trade-off between ease of training,
robustness to hyperparameters, model expressiveness and generalization
capabilities, and are therefore advised when dealing with SL problems on
tabular data. Conversely, when dealing with unstructured data (such as images,
graphs, point clouds, meshes, etc.), deep learning (DL) model architectures
become more prominent. Specifically, while the multi-layered perceptron (MLP)
is a popular “one fits all” DL architecture, it is advised that the choice of
a DL architecture is motivated by the presence of the appropriate “inductive
biases” for the given data [155]. A relevant example are convolutional neural
networks (CNN), which are among the state-of-the-art for Computer Vision
problems [156]. By learning local filters that are convolved with spatial 2D
feature maps, CNNs are able to efficiently exploit the spatial correlations in
image data, i.e., the CNN architecture possesses the right inductive bias for
processing image data. Similarly, Recurrent Neural Network (RNN) architectures
such as Long Short Term Memory networks (LSTM) [157] and Gated Recurrent Units
(GRU) [158] possess an inductive bias that makes them effective for processing
sequence data. Note that an inductive bias, while desirable, is not mandatory
for learning an effective model. In this regard, Transformer architectures
[159] (the popular ChatGPT model [160] is one such example) have completely
superseded RNNs in learning from large-scale natural language thanks to their
expressiveness, scalability and ease of parallelization, albeit not possessing
any particular inductive bias.

A recent family of models particularly relevant for propagation pertains to
the field of Geometric Deep Learning [161]. Indeed, it can be argued that the
laws of geometry are pervasive in propagation (e.g., in RT algorithms and
Stochastic Geometry), such that many fundamental algorithms in propagation
operate on data that displays some geometric properties. In this regard,
Geometric Deep Learning aims to devise model architectures able to exploit the
underlying geometrical properties of the input data. The aforementioned CNNs
are a prominent example of Geometric Deep Learning applied to Euclidean
domains (i.e., 2D feature maps). A generalization of CNNs to non-Euclidean
domains are Graph Neural Networks (GNNs) [155, 161], which have been applied
with success to physics simulation [162] and processing of 3D point cloud data
[163]. As such, Geometric Deep Learning holds an untapped potential for
breakthrough applications in propagation.

Finally, one major limitation of complex ML models is their lack of
interpretability. Specifically, while tree-based models retain some degree of
interpretability (e.g., by the means of feature importance), DL models behave
fundamentally as black-boxes, which may hinder their deployment in risk-
sensitive application scenarios. With the goal of opening said black boxes and
deriving precious insight on the learned knowledge, several eXplainable AI
(XAI) algorithms have been developed in literature. A prominent example are
SHapley Additive exPlanations (SHAP) [164] which, given an arbitrary black-box
function, computes the impact of every individual input feature to the final
output. XAI approaches for specific model architectures have also been
developed. For instance, GradCAM [165] interprets CNNs by highlighting on the
input images where the model ”looks” for taking a decision. GNNExplainer [166]
interprets GNNs by deriving the subgraphs of the input graph that are the most
influential for the output predictions. Recent advances in ML
interpretability, particularly relevant for propagation, are Symbolic
Regression algorithms [167]. Briefly, the task in Symbolic Regression consists
in finding the mathematical expression, modeled as a sequence of tokens (i.e.,
mathematical operators and physical quantities) that provides the best-fit to
the data, balancing the delicate trade-off between goodness-of-fit and
complexity of the derived expression. The associated optimization problem is
of combinatorial nature: as such, RL can be leveraged for efficiently
exploring the space of all possible symbolic expressions. Overall, model
interpretability can provide precious insight on the underlying physical laws
present in raw measurement data, complementing domain-specific knowledge.

##  8 Channel parameter estimation

  * by Wei Fan, Xuesong Cai, Diego Dupleich, Ruisi He, and Bo Ai

###  8.1 Parameter estimation techniques

Channel parameter estimation aims at extracting propagation parameters such as
propagation delay, angles, polarimetric amplitudes, etc. of path components
from the measurement data. COST INTERACT has contributed to the development of
such algorithms. For example, the authors in [168] proposed a maximum-
likelihood estimation algorithm to deal with channels that exhibit a mixture
of independent dense multipath components (DMCs), which is in contrast to the
commonly assumed model of single DMC mode. An auto-encoder was proposed to
infer the order of DMC modes and for initializing the parameters of the DMC s.
In general, channel parameter estimation techniques can be mainly classified
into four categories: spectra-based techniques, subspace-based techniques,
sparsity-recovery-based techniques, and maximum-likelihood-based techniques.

####  8.1.1 Spectra-based techniques

Bartlett beamforming is the most classical spectra-based method, aiming to
find the direction(s) with dominant power(s) [169]. Its variant, a frequency-
invariant beamformer for uniform circular arrays can also be found in [170].
The basic idea was to pre-compensate to frequency-dependent phase variation so
that two dimensional (2D)-fast Fourier transform (FFT), i.e., beamforming, can
be efficiently applied to finding the dominant paths in azimuth and delay
domains jointly. For mmWave frequency bands, a widely applied spectra-based
method is to obtain the joint angle-delay spectrum according to the channel
measurements by rotating horn antennas to different directions [171, 172, 173,
83]. These methods are straightforward and with relatively lower complexity.
However, system responses such as antenna radiation patterns are usually
embedded in the resulting spectra, making it difficult to separate the
propagation channels from the sounder hardware, not to mention the low
resolutions.

####  8.1.2 Subspace-based techniques

Among the subspace-based methods, the well-known ones are MUltiple SIgnal
Classification (MUSIC) [174] and unitary Estimation of Signal Parameter via
Rotational Invariance Techniques (ESPRIT) [175], and their variants can be
found in, e.g., [176, 177, 178, 179, 180]. In the early development of these
techniques, the basic assumption is that propagation paths are uncorrelated so
that the covariance matrix of the received signals can be decomposed into
signal subspace and noise subspace [174]. By examining the distances
(orthogonality) between steering vectors and the noise space, a spectrum can
be obtained with its peaks indicating path directions. Intuitively, if a
steering vector is orthogonal to the noise space, it means that it belongs to
the signal space, i.e., contributing to the received signals. Later on, these
techniques were extended to dual domains such as delay vs. frequency
covariance matrix. The main limitation is the deficiency in resolving a large
number of paths and requiring a certain number of snapshots to obtain a sample
covariance matrix.

####  8.1.3 Sparsity-recovery-based techniques

Sparsity recovery algorithms [181, 182] are developed based on the assumption
that the channel exhibits sparsity in parameter domains, i.e., only a few
paths contributing to the received signals, although the assumption is still
questionable even in mmWave frequency bands [183]. By exploiting specific
optimization principles, e.g., convex optimization, channel parameters can be
recovered.

####  8.1.4 Maximum-likelihood-based techniques

Despite the high complexity of maximum-likelihood-based estimation algorithms,
they can extract the propagation parameters that are properly defined in the
signal model with a high resolution that approaches the theoretic lower error
bound, i.e., Cramér-Rao Lower Bound (CRLB). The most widely used maximum-
likelihood-based algorithm is the SAGE algorithm [184]. It is a further
enhanced algorithm based on the Expectation-Maximization (EM) principle [185]
that is theoretically proven to converge to a local maximum of the likelihood
objective function. In SAGE, the high-dimensional estimation/optimization
problem can be decomposed into several one-dimensional problems, leading to
much lower complexity and faster convergence. In [186, 187, 188], variants of
the SAGE algorithm can also be found for mmWave wideband large-scale arrays,
where the trajectories in delay domain across the array aperture are exploited
for effective initializations and interference cancellation. The SAGE
algorithm usually assumes that the propagation paths are well-resolvable.
However, it is possible that due to the scattering effect of rough surfaces,
the resulting MPC s can be very dense in delay and angle domains that cannot
be well resolved by the intrinsic ability of the sounder. In such cases, one
needs to modify the signal model to consider these dense MPC s, i.e., DMCs, as
colored noises (in addition to white Gaussian noises) with certain power
profiles for a better estimation of other discrete MPC s, which is basically
the RIMAX algorithm [189]. Although there exist not a few different parameter
estimation techniques, future mmWave and THz propagation channels will pose
more challenges due to much larger bandwidths, much larger array apertures,
etc. Spherical-wave propagation, channel birth-death on the array, frequency-
dependent responses of antennas and sounders, etc., will make the signal model
much more complicated, meaning that low-complexity yet still high-resolution
estimation techniques are still in need.

Another important consideration for parameter estimation is that the system
response of the channel sounder must be well characterized for a realistic
signal model. Otherwise, the mismatch of the signal model from reality can
result in many ghost (erroneous) paths estimated [27]. The response of cables,
power amplifiers, filters, converters, etc., of the channel sounder can be
easily calibrated through back-to-back measurements, i.e., directly connecting
Tx and Rx without antennas. For antennas or antenna arrays, they can be placed
in an anechoic chamber to measure the 3D responses of antenna elements at
discrete angles. The measurement data can be later on exploited to interpolate
the responses at arbitrary angles. There are different ways to do this, which
include direct (linear or non-linear) interpolation, spherical harmonics, and
Effective Aperture Distribution Function (EADF) [190]. Direct interpolation is
straightforward but non-analytic. Alternatively, one can transform the
measured pattern from the spatial domain to another domain using basis
functions, either spherical harmonics or Fourier basis (EADF). Using a forward
transform, the transformed spectra are obtained and then utilized to recover
spatial patterns. There is also a possibility to compress the measurement data
if the spectra are concentrated so that the unimportant components in the
transformed domain can be removed. A practical application of EADF in dynamic
mmWave channel sounding using 128$\times$256 switched arrays can be found in
[60].

####  8.1.5 Future challenges and directions

6G is envisioned to support applications beyond the current 5G mobile use
scenarios, which will pose stringent requirement on the communication systems
in terms of data-rate, latency, reliability, and so on. To meet those
demanding requirements, a raft of advanced radio technologies are envisioned,
e.g. utilization of higher frequency spectra (e.g. up to sub-THz frequency
bands), higher system bandwidth (e.g. up to a few GHz), and larger-scale
multi-antenna systems (e.g. large-scale or extremely large-scale antenna),
utilization of RIS, etc. The advancement in 6G radio technologies have posed
significant challenges to the channel estimation parameter. For example, the
extremely large scale antenna array will bring a few new challenges for the
channel parameter estimation: near-field and spatial non-stationary effects.
State-of-the-art algorithms, which are developed based on plane wave model and
spatial stationary channel might fail to address all the new features
introduced by the massive MIMO systems. The narrow-band assumption might also
be violated for 6G radio systems that potentially utilize the ultra-wide-band
technology, which should be properly considered for developing channel
parameter estimator. As for the sub-THz/mmWave channel measurements, the
required phase accuracy might be difficult to achieve, though a few works on
phase compensation concept have been reported to achieve coherent phase
measurement for virtual antenna array systems. Therefore, it would be
desirable to develop channel parameter estimators, which is robust to phase
measurement inaccuracy.

###  8.2 MPC Clustering

Multipath clustering and identification have been important for channel
modeling. ML naturally meets the need to group multipath components (MPCs)
with similar channel characteristics, and some well-designed clustering
algorithms which naturally incorporate propagation characteristics have drawn
great attention [191, 146] for cluster-based channel modeling, and many
researches have been conducted in COST INTERACT. Clustering algorithms can be
generally categorized into: i) Shape-based; ii) Distance-based; iii) Density-
based; and iv) Computer vision-based clustering.

####  8.2.1 Shape-based clustering

Shape-based clustering involves revealing cluster structure of MPCs in power-
delay domain. For example, MPCs belong to the same cluster are supposed to
have a PDP that follows a single exponential decay, according to the Saleh-
Valenzuela channel model. To recognize clusters in PDP, a fitting method is
used to check if envelope shape of MPCs matches a particular distribution, as
shown in Fig. 14(a). This method adjusts cluster members by seeking the best
fitting result and it is able to accurately identify MPC cluster [192].
Several shape-based methods have been proposed: i) training a hidden Markov
model (HMM) to learn distribution of MPCs in PDP and optimizing cluster
members using the Viterbi algorithm; ii) using an observation window to
separate large cluster and applying a threshold of slope to improve clustering
accuracy on small clusters; iii) using kurtosis to measure peakedness of a
distribution and applies region competition to divide MPCs into different
clusters [193]. In summary, shape-based cluster identification has several
advantages including not requiring much prior knowledge about number of
clusters and having a relatively low computation complexity. However, its
limitation is the lack of angle information during clustering, which can
impact identification accuracy.

####  8.2.2 Distance-based clustering

Distance-based clustering measures similarity between different MPCs based on
distance , which is defined in terms of delay, angle of arrival (AoA), and
angle of departure (AoD), as shown in Fig. 14(b). Commonly used distance
measures for MPC clustering include squared Euclidean distance (SED),
normalized Euclidean distance (NED), and multipath components distance (MCD).
SED focuses on natural difference between each parameter, while NED focuses on
ratio difference. Sequential clustering-based algorithms have been proposed to
identify MPC clusters using SED. However, to compare parameters in different
domains, it is necessary to normalize parameters to the same scale. MCD has
been proposed to address this issue by normalizing delay and angle before
calculating distance, which is found to have fairly good performance [194].
Further, many distance-based clustering algorithms such as hierarchical tree
clustering, K-power-means (KPM), and fuzzy-C-means (FCM) have been proposed
and widely used.

Figure 14: Illustration of different clustering algorithms.

####  8.2.3 Density-based clustering

In the natural propagation environment, MPC are grouped and the MPC near
cluster centroid usually have higher density than those at the edge of
cluster. Density-based clustering algorithms, such as DBSCAN, can identify MPC
clusters based on distribution and density property, as shown in Fig. 14(c).
These algorithms calculate MPC density for clustering and do not require prior
knowledge such as cluster number and cluster centroids initial position.
However, how to measure MPC density is important to algorithm performance. To
obtain improved performance, [195] firstly proposes the perspective to
consider physical propagation mechanism for clustering algorithm by designing
MPC density. It proposes a novel aspect of MPC kernel-power-density to well
incorporate the modeled propagation behavior of MPCs into clustering
algorithm, and it obtains significant performance gain in terms of low
computational complexity and high clustering accuracy.

####  8.2.4 Computer vision-based clustering

Computer vision-based clustering exploits image processing methods to identify
MPC clusters based on visual criteria such as shape of potential cluster,
distribution pattern of MPC delay and angle, as shown in Fig. 14(d). One
example is the Hough transform-based clustering, which uses the Hough
transform to recognize trajectory of MPCs in delay domain and merges the
recognized trajectory into clusters. Another example is the PAS-based
clustering and tracking (PASCT) algorithm [196], which uses the maximum-
between-class-variance method to separate potential cluster groups from
background noise and further divides the clusters by using density-peak-search
method. These methods follow an intuitive approach and provide results that
conform to human observation whereas also benefit from the rapid development
of computer vision.

####  8.2.5 Future Work

Many long-standing problems remain unsolved on this topic. For example, for
time-varying non-stationary channels, the existing algorithms still need to be
improved significantly. Supervised AI-based clustering and tracking methods
are worth receiving more attention in the future, especially considering the
phenomenal increase in both the amount of collected channel measurement data
and the available computing power. Moreover, drawbacks still exist with
respect to algorithm complexity, threshold choices, and assumptions about
prior knowledge. Algorithms with few prior knowledge about clusters should be
further developed.

##  9 New technologies

  * by Joonas Kokkoniemi, Marco di Renzo, Narcis Cardona, Ruisi He, Bo Ai, Wei Fan, Xiping Wang, Ke Guan, Tomaz Javornik, Franco Fuschini, Yang Miao, Mi Yang, Dan Fei, Guido Valerio, and Julien Sarrazin

###  9.1 Path Loss Measurements and Modeling for RIS

In recent years, RIS s have been under active discussion and investigation as
a promising technology to enable future 6G networks. RIS s are artificial
electromagnetic surfaces comprised of large numbers of sub-wavelength unit
cells or antenna elements. Those are either very small elements ($\ll\lambda$)
forming a larger artificial surface (a metasurface) or individually
controllable antenna elements such as in reflect arrays. The RIS s can
flexibly control the parameters of wireless signals, such as phase, amplitude,
and polarization, thus enabling the emerging concept known as “smart radio
environment” [197]. The core principle of the RISs is to be able to capture a
part of the radiated power with the aperture of the RIS and redirect it via
reflection towards wanted direction by manipulating the phase (and amplitide)
of the insident radiation. Usually the goal is to do beamsteering the amplify
certain directions, but as mentioned above, it is also possible to manipulate
the signal itself. Therefore, the RISs allow the manipulation of the radio
channels. This is traditionally impossible and usually the only way to
manipulate the signals is via antennas and arrays of those. Hence, the RISs
give an interesting opportunity for added control that can be used to overcome
problems, such as signal blockages and cell edge signal amplification.

Two important use cases for RISs in the literature are shoot-through RIS and
reflective RIS. The former can be used for beamforming close to antenna, but
the latter is more important what comes to channel modeling and especially
manipulation of the channel coefficients. In this section, we talk exclusively
about reflective RISs and the channel modeling related to those. So far, some
of the challenges with RIS channel models is the rather limited number of
physical prototypes and multitude of options and ways to manufacture RISs.
Later in this section we give some examples of the channel modeling activities
related RISs.

####  9.1.1 RIS channel modeling challenges

Generally speaking, some of the challenges in RIS channel modeling arise from
the fact that the behavior of the RIS itself depends on how it’s made and how
it as been configured. The basic communication scenario is that we have a Tx,
RIS, and an Rx. The channel between Tx and RIS and RIS and Rx are traditional
channels, e.g., free space of fading channels. The phase shifts introduced by
the RIS are then optimized minimize the total loss in the cascaded Tx-RIS-Rx
channel. Because the RIS is an active element, often assumed to be controlled
by the base station, a closed formed macroscopic channel models are hard to
derive as the RIS by definition reconfigures the channel coefficient based on
the particular situation. Therefore, also the total path loss depends on the
entire system setting and outcome of the optimization problem.

Whereas wireless communication engineers in the past have resorted to
macroscopic channel models in system analysis and optimization, there are also
other challenges what comes cascaded RIS channels. For instance, the real
systems are almost never random. The network engineers design the network
based on the maximization of the signal power in some local are and
availability of locations for the base station and supporting hardware. In the
case of RISs this can mean that the engineer may try to arrange a very good
LOS channel between the base station and RIS whereas the RIS-user channel can
be normal mobile fading channel, for instance. But this is just one of many
options from network design point of view and the individual channels depend
on the location and how the network elements can be placed. This makes the
general channel modeling very challenging and the channel losses and potential
gains achieved by RISs depend on the deployment environment. Especially in
rural setting where distances are long, RISs are most likely not going to
provide good gain as the reflected energy decreases fast with distance.
However, in the case of urban and indoor scenarios where RISs are expected to
be the most beneficial, there are powerful tools avaialable to test and
optimize networks via simulations.

Ray tracing has become an important tool to study channel especially at high
frequencies where traditional channel modeling is challenging due to difficult
channel measurements. As the ray tracing relies on fixed 3D maps, they have
also been used extensively in network design in order to optimize the network
element placement. Ray tracing is therefore also a very good tool in studying
RIS channels in various scenarios, e.g., as shown in [198] in urban
environment. The downside of the ray tracing is that the simulation always
require accurate 3D models of the desired environment. The upside is that
testing the network elements in simulation environment is very flexible and
fast. Therefore, the ray tracing is very good tool to test and evaluate the
RISs as well. This is somewhat related to digital twins in which we have a
replicated digital environment where we can test, e.g., RISs, but
theoretically also optimize those in real time in the case of actual physical
deployment.

Ray tracing is particularly powerful in system evaluations and performance
testing, but can be locally used for channel modeling as well. The challenge
in channel modeling tends to be that the results are representative for that
particular environment, but not in general. However, it is still much faster
to generate data with ray tracers that by real measurements. Therefore, an
appealing option is to calibrate ray tracers with real measurement data in
order to extend the measured data. In the case of RIS this still does not take
away the difficulty of statistical modeling of an active reconfigurable
element that can take many forms and sizes. Ray tracing can still be very
efficiently used in evaluation of the RISs in the desired environment in order
to deside where to place it/them and what kind of gain do those give in the
scenario in hand. Whereas there are too many channel scenarios to discuss
herein where RISs could be useful and what those require from the channel
modeling, in below we give some examples of the works related to RIS channel
modeling.

####  9.1.2 RIS channel models from literature

The path loss and propagation are basic characteristics of wireless channels.
The channel represents the basic relation between the wireless signal power
and the transmission distance, which can provide information on how far a
wireless signal can be successfully transmitted. Due to the importance of
characterizing the path loss and channel reciprocity characteristics of RIS-
assisted wireless communications, researchers have recently conducted related
studies that are based on different analytical assumptions and approaches
[199],[200].

Di Renzo et al. [201] derived asymptotic scaling laws of the path loss as a
function of the transmission distances and the size of the RIS in the far-
field and near-field cases. The results are obtained by leveraging the scalar
Huygens-Fresnel principle in a two-dimensional space. Garcia et al. [202]
calculated the radiation density of the scattered field in the near-field and
far-field under the assumption of dipole antennas and discussed the scaling
laws as a function of the transmission distances numerically. Ellingson [203]
proposed a physical model for the path loss of an RIS-assisted wireless link
under the assumption that the antenna gain of the transmitter/receiver is
constant over the RIS. Najafi et al. [204] developed a physics-based RIS path
loss model, in which the impact of grouped unit cells on the wireless channel
is obtained by solving the integral equations for electromagnetic vector
fields under the far-field assumption. Danufane et al. [205] generalized the
model in [201] to a three-dimensional space by using the vector generalization
of Green’s theorem, and characterized the scaling laws of the path loss as a
function of the transmission distances and the size of the RIS based on
scattering theory. Wang et al. [206] proposed a radar cross section-based path
loss model that introduces an angle-dependent reflection phase behavior of RIS
unit cells. Gradoni and Di Renzo [207] developed a path loss model that is
based on the theory of mutual impedances of thin dipole antennas. The end-to-
end channel model resembles a MIMO communication system and considers the
mutual coupling among RIS unit cells. Recently, Di Renzo et al. [208] proposed
a path loss model, based on scattering theory, assuming that the incident
signals are not constant over the RIS elements. A summary of the above-
mentioned research works on RIS path loss modeling is available in Table I of
[209].

###  9.2 Channel Measurements and Modeling for ISAC

In the last decade, radar has been considered in some works as the natural
complement to communications, in which the radar-based sensing is a default
technology. In [210] a low-complexity algorithm for joint radar and
communications in automotive is developed, while in [211] the sensing from
radar is used to assist the beam-steering at mmWave links. The sensing
information is used to predict the communications channel in
[212].Nevertheless, when we refer to ISAC inthis paper, it is done for
communications-centric ISAC, meaning that sensing is implemented using the
communication signals from one or more radio access nodes. To this end, in
COST INTERACT, authors in [213] are developing solutions for joint sensing and
communications (JSAC), with parallel sensing and communication waveforms that
share the same bandwidth.

ISAC has become a design paradigm for 6G, for which understanding and modeling
the behaviour of the sensing and the communications channels simultaneously is
crucial. The main ISAC applications in 6G mobile networks are vehicular
communications (V2X), sensing as a service, remote sensing and environmental
monitoring, while those applying to short distance wireless channels are,
among others, in-cabin sensing, smart home, and human-computer interaction
[21, 214, 215]. This confers different ISAC channel scenarios, which can be
summarized into three types (Fig. 15): monostatic, bistatic, and distributed,
depending on the deployment of the transmitter and receiver(s).

####  9.2.1 Sensing scenarios: monostatic, bistatic and distributed

ISAC scenarios are monostatic when sensing transmitter and receiver are at the
same position; bistatic case refers to sensing transmitter and receiver being
separated, while distributed scenarios account for multiple sensing paths
available for the same target. In [216] a description of the monostatic,
bistatic and distributed ISAC concepts, and the advantages of the centralized
version of ISAC, are discussed. Figure 15 summarises the three cases.

Figure 15: Monostatic, Bistatic and Distributed ISAC scenarios

For the monostatic case, many radar concepts apply, like the radar cross
section (RCS) to measure the radar reflectivity of individual targets.
Monostatic sensing channels can anyway be modeled from the multipath
propagation, limited to the case of having the transmitter and receiver at the
same position, so most of, but not all, the sensing energy coming from LoS
components. To that end, [217] provides an empirical scattering model obtained
from measurements of materials at 28 and 140 GHz. The implementation of
monostatic ISAC sensing requires the full-duplex mode of the radio interface,
which is not implemented so far in current 3GPP standards. This is the reason
why some proposed systems for ISAC refer in fact to a JSAC strategy, in which
a radar sensor is used to improve communication links.

The estimation of the communications channel from the sensing one is
investigated in [218], where the authors consider the communication receivers
to be the targets of a monostatic OTFS-based ISAC system. Thus, the two
channels in this case are highly correlated.In [219] the authors use OTFS
modulation for joint radar parameter estimation and communications, exploiting
the channel sparsity in the Delay-Doppler domain for efficiently separate
sensing (radar) parameters estimation and communications.

In the bistatic case, the transmitter sends communication and sensing signals
and the receiver captures the echoes from targets and clutter together with
the multipath (scattered) components that compose the communication channel.
As a consequence, any model that describes simultaneously the sensing and the
communications channels has to be geometrically accurate for simultaneous
multiple links, and reproduce moving targets along different tracks with
spatial consistency, including phase continuity [216].

####  9.2.2 Trends in ISAC channel modeling

The ISAC channel models are a combination of two channels in one single
geometry, i.e., a common framework that develops two approaches, either
simulated with more or less precision, or stochastically created. In [220],
the ISAC channel is defined as a two-port system in which sensing and
communication channels, as well as the common interference between them, are
modeled as the reflectivity and transmission functions of such system. This
scheme is adaptable to any current model in the literature, either
deterministically created by precise 3D ray-tracing, stochastically from a
random distribution of scatterers, targets, and clutter, or a hybrid of the
two.

##### Stochastic models

Recent approaches on ISAC stochastic channel modeling propose GSCM extensions
to the 3GPP channel model [111], e.g., within COST INTERACT [221] and
elsewhere [222], [223]. The stochastic channel modeling methods dominated the
evaluation of wireless communications in 5G due to their low computational
complexity and easy standardization, which was sufficient to evaluate the
communication performance of 5G use cases. The application of GSCM models to
ISAC requires adding some important elements to the model, since communication
and sensing channels are somewhat different. For example, [223] includes both
stochastic and deterministic approaches while accounting for spatial
consistency. In [221], paths are generated by probabilistic functions derived
from channel measurements made in real scenarios, thus geometrically pre-
setting the distribution of effective scatterers, i.e., the objects on which
the set of rays is incident. The authors in [223] propose a shared cluster-
based stochastic JSAC channel model and conducts a channel measurement
campaign in typical LoS and NLoS indoor scenarios at 28 GHz and obtains the
power angular delay profiles (PADPs) of the communication and sensing
channels. For monostatic case, the correlation between the channels may exist,
but this is not so obvious correlation for bistatic and distributed cases,
mainly because sensing “targets” and communication “scatterers” are not
necessary the same objects or, if they are, do not have the same reflectivity
behaviour.

##### Deterministic models

Current deterministic channel models have modeled scatterers for
communications and have considered the reflection from them in the form of
specular reflection or diffuse scattering. From radar/sensing perspective, any
target in the scene can be modeled according to its RCS, but for ISAC, more
detailed parameter are required, e.g., the bistatic target reflectivity (BTR)
[216]. An example of a target is a person on a bike (Fig. 15), a moving target
with local movements, causing in terms of sensing a long-term Doppler because
of its displacement, and micro-Doppler originated by the person and wheels
movements [216]. The same work further establishes that, for ISAC,
deterministic models with more physical descriptors are needed, especially for
target modeling, in contrast to the statistical approach of the GSCM models.
To resolve the issue, the authors propose a propagation channel model for ISAC
that is geometrically correct for multiple simultaneous sensor links and