3GPP

    the third generation partnership project
5G

    fifth generation
6G

    sixth generation
AI

    artificial intelligence
AoA

    angle-of-arrival
AoD

    angle-of-departure
BS

    base station
D-MIMO

    distributed multiple-input multiple-output
EM

    electromagnetic
GDOP

    geometric dilution of precision
GNSS

    global navigation satellite system
IQ

    in-phase and quadrature
JCS

    Joint Communication and Sensing
JRC

    joint radar and communication
JRC2LS

    joint radar communication, computation, localization, and sensing
ICI

    inter-carrier interference
IOO

    indoor open office
IoT

    Internet of Things
IRN

    infrastructure reference node
KPI

    key performance indicator
LoS

    line-of-sight
MIMO

    multiple-input multiple-output
mmWave

    millimeter-wave
NLoS

    non-line-of-sight
NR

    new radio
OFDM

    orthogonal frequency-division multiplexing
OTFS

    orthogonal time-frequency-space
PRS

    positioning reference signal
QoS

    Quality of Service
RAN

    radio access network
RAT

    radio access technology
RedCap

    reduced capacity
RIS

    reconfigurable intelligent surface
RTK

    real-time kinematic
RTT

    round-trip-time
SLAM

    simultaneous localization and mapping
SNR

    signal-to-noise ratio
SOTA

    state of the art
ToA

    time-of-arrival
TDoA

    time-difference-of-arrival
TR

    time-reversal
TRP

    transmission and reception point
TX/RX

    transmitter/receiver
TX

    transmitter
RX

    receiver
UE

    user equipment
multi-RTT

    multi-cell round-trip-time
UL-TDOA

    uplink time-difference-of-arrival
DL-TDOA

    downlink time-difference-of-arrival
UMi

    3D-urban micro
UMa

    3D-urban macro
FR1

    frequency range 1
FR2

    frequency range 2

# Positioning and Sensing in 6G:
_Gaps, Challenges, and Opportunities_

Ali Behravan, Vijaya Yajnanarayana, Musa Furkan Keskin, Hui Chen, Deep
Shrestha, Traian E. Abrudan,
Tommy Svensson, Kim Schindhelm, Andreas Wolfgang, Simon Lindberg, and Henk
Wymeersch

###### Abstract

Among the key differentiators of 6G compared to 5G will be the increased
emphasis on radio based positioning and sensing. These will be utilized not
only for conventional location-aware services and for enhancing communication
performance, but also to support new use case families with extreme
performance requirements. This paper presents a unified vision from
stakeholders across the value chain in terms of both opportunities and
challenges for 6G positioning and sensing, as well as use cases, performance
requirements, and gap analysis. Combined, this motivates the technical
advances in 6G and guides system design.

###### Index Terms:

6G, Positioning, Sensing, Use cases, Gap analysis.

##  I Introduction and Motivation

Large bandwidth and massive arrays employed in the emerging wireless
communication networks along with network densification enable additional
services, such as radio based positioning and sensing, which are beyond data
transmission, with minimal cost by using the same infrastructure and spectrum.
Positioning of active communication devices has become an integral part of the
recent and ongoing standards, such as in the third generation partnership
project (3GPP) and IEEE [1]. Position accuracy requirements have also been
increasing from tens of meters, as mandated by regulatory agencies, to
decimeter level for the future use cases such as indoor factories, unmanned
aerial vehicles (UAVs), vehicle to everything (V2X), etc [2]. On the other
hand, positioning of passive targets, i.e., sensing of objects that do not
transmit (only reflect/scatter) radio signals has not yet been included in
3GPP standards. Radio based sensing covers a broad class of applications such
as radar-like range and Doppler estimation, radio imaging, environmental
monitoring and material identification. Hence, there is no single performance
indicator and requirement that can be defined for a sensing service. As will
be discussed, different use case families have different and new key
performance indicators and require varying levels of sensing accuracy.

In addition to supporting new use cases, another important motivation of
integrated positioning and sensing in a mobile communication network is that
such information about the environment can also be used to improve the
communication performance. As an example, a digital twin of the environment
that is created by sensing, can be used to aid communication functions such as
radio resource management, beamforming, mobility management, minimization of
driving test, etc.

In a recent 6G localisation and sensing study conducted by the authors for the
European Union Hexa-X project [2], the potential of sensing with radio waves
to enable new use cases and applications as well as improve communication
aspects of the 6G systems are investigated. In this article, we highlight the
key findings from these studies providing a detailed gap analysis for
positioning and sensing use cases in sixth generation (6G), as well as
envisioned 6G radio enablers and challenges, which serve to motivate continued
research in this area.

Figure 1: Positioning of a UE with several BSs (left) and sensing of an object
(right). Positioning using TDoA from 3 BSs is shown in red dashed lines,
constraining the UE to lie on the intersection of 2 hyperbola in 2D.
Alternatively, DL-AoD (or UL-AoA) form lines which intersect at the UE
location. Combinations of TDoA and AoD are also possible. Monostatic sensing
by the BS (in DL) and the UE (in UL) is shown in black, while bistatic sensing
is shown in green.

##  II Mobile Radio Positioning and Sensing

In this section, we provide an overview of the fundamentals of mobile radio
positioning and sensing.

###  II-A Positioning and Sensing Fundamentals

Positioning is the process of estimating the location (and in some cases,
orientation and velocity) of a device from radio measurements such as received
signal power, time-of-flight between transmitter and receiver, direction of
the signal, or any combination of those, as illustrated in Fig. 1 (left part).
The position estimation performance depends on the resolution and accuracy of
the underlying measurements, number of base stations involved, and relative
positions of the BSs with respect to the user equipment (UE). The cellular
positioning reference signals employed in 5G include downlink positioning
reference signal (DL-PRS) and uplink sounding reference signal (UL-SRS). Among
the time and angle positioning methods, uplink/downlink time-difference-of-
arrival (UL/DL-TDoA) use SRS/PRS, respectively, uplink angle-of-arrival (UL-
AoA) uses SRS, and downlink angle-of-departure (DL-AoD) uses the beam index,
whereas multi round trip time (multi-RTT) relies on both PRS and SRS [3]. In a
typical 5G network, signaling information that is intrinsic to the network,
such as serving cell, serving beam, timing-advance and reference signal
received power (RSRP), can be exploited to estimate location information.
Methods using such information are called _enhanced cell ID (e-CID) methods_.
The reference signals such as DL-PRS and UL-SRS can be employed to compute
time-difference-of-arrival (TDoA) between a pair of reference nodes (e.g.,
BSs), which can in turn yield a locus of points along a hyperbola in which UE
may be present. Thus, TDoA measurements from multiple pairs of reference nodes
can triangulate to a precise UE location. These are broadly termed as _TDoA-
based multilateration methods_. In addition, 5G mmWave operation enables large
dimension MIMO, which can provide high angular resolution, thereby enabling
precise angle information between the reference node and the UE. The angle
measurements between the UE and multiple reference nodes can be exploited to
arrive at the position of the UE. These are usually termed as _angle-based
positioning methods_. To avoid the need for tight synchronization in TDoA,
_round trip time (RTT) based methods_ from multiple BSs have also been
introduced [1].

Sensing is the process of detecting and tracking targets such as vehicles,
obstacles and humans, etc., and estimating their relative range, velocity,
size, shape, orientation or material properties. Depending on where the
transmitter(s) and receiver(s) are placed, the sensing process can be
classified as mono or bi/multi static sensing, as depicted in Fig. 1 (right
part). The 5G standard does not explicitly specify methods, signaling, and
protocols for sensing; however, there have been some attempts in research to
exploit 5G signals for sensing [4]. Considered use cases in these works
include activity detection, presence detection, etc. It is envisaged that 6G
may support new signaling, protocol and methods similar to positioning to
support emerging new use cases for sensing.

##  III Use Cases and Requirements

Potential use cases for the future wireless generation can be categorized into
5 use case families. An overview of these use case families together with the
main positioning and sensing KPIs is presented in Fig. 2 and elaborated below.
Further details of the use cases together with some of the most commonly used
metrics used for sensing and positioning are available in [2] and references
therein. Use cases are not fully orthogonal across the use case families.

Figure 2: Use case families for 6G, covering a wide span of communication,
positioning, and sensing requirements, together with the corresponding KPIs
for positioning and sensing.

##### Sustainable development

This use case family encompasses 6G use cases that address the sustainable
development of society and, at the same time, reduce the environmental impact
of different industries. One such use case is providing healthcare for all,
regardless of geographical location. Remote healthcare is one such enabler
which requires a rather fine location accuracy (e.g., drone deployment for
medical sample collection). The required latency, in this case, is rather
relaxed and a moderate availability is enough for a service guarantee. Other
example use cases are remote sensing and monitoring of weather conditions,
keeping track of biodiversity around the globe, and also asset tracking, all
of which induce rather relaxed accuracy (meter-level) and latency (sec-level)
requirements.

##### Immersive telepresence

Sensing and positioning can improve immersive telepresence for enhanced
interactions. This family of use cases includes gesture recognition for human-
machine interactions and augmented reality (AR). Gesture recognition requires
both rather fine range and also angular accuracy. Depending on the specific
use case, AR may have different requirements. For example, while AR for
providing context-aware services such as a shopping mall experience requires
moderate location and angular accuracy, AR for placing an object in the real
world requires cm-level location and tight orientation accuracies.

##### Local trust zones for humans and machines

Another use case family that can utilize sensing and positioning is
information security and providing local trust zones for humans and machines.
One part of this family includes use cases with very tight location and
orientation, availability, and latency requirements, such as telesurgery,
localizing micro-robots within human body, as well as placement of medical
equipment on the body. Another part of the family includes patient tracking
and monitoring, sensor infrastructure web to support devices without sensing
capability, cooperative positioning to support devices with little or no
network coverage, and providing temporary local coverage when coverage from
planned network infrastructure is not available, which can operate with more
relaxed location accuracy, etc.

##### Massive twinning

Providing an efficient digital twin of objects and events in the digital
domain can open up a host of new possibilities. One possible use case is in
manufacturing, where configuring and using industrial tools can be done
remotely. This type of use case requires location and range resolution in the
order of centimeters. Also, this use case requires a good velocity resolution
in the order of a fraction of a meter per second for moving objects. Another
area where twinning can provide value is immersive smart cities, wherein one
scenario, obtaining a real-time digital twin of the city can help optimize
utilities. Another scenario in this area is traffic monitoring. Smart city use
case requires meter-level to sub-meter level location accuracy, and rather
relaxed latency and availability requirements. On the other hand, the digital
twin of a smart building, where the location of each power switch, lamp,
heater, etc., is important for effective interaction, requires more precise
positioning, while tolerating longer delays and less availability. The most
stringent requirement is the high scalability requirement.

##### Robots to cobots

This final use case family includes solutions that can enable collaborative
robots (cobots), such as positioning of robots, obtaining a map of their
environment, sensing objects, and fine positioning of vehicles around them.
Some positioning use cases in this family such as localizing collaborative
robots require very accurate position down to cm level, and also low latency
requirements. Sensing applications, such as environmental mapping of robots,
have tight sensing location accuracy requirements as well as velocity
resolution, and angular resolution.

Figure 3: Accuracy, latency, and availability requirements for positioning use
cases. The diagonal lines represent the maximum tolerable latency under the
specified mobility, so that the accuracy requirement can still be met. Latency
is determined based on the mobility, such that devices/objects move only 10%
of the target accuracy.

Fig. 3 summarizes the requirements on accuracy, maximum latency, and
availability for different use cases. The color-coding of use cases represent
their availability requirements. These requirements have been derived based on
the available literature (the full list is available in [2, 5]) and data
provided by industry organizations.

Table I shows the requirements on location accuracy, range resolution, and
velocity resolution for sensing use cases.

TABLE I:  Requirements for sensing use cases. Use case |  Location accuracy [m] |  Range resolution [m] |  Velocity resolution [m/s]
---|---|---|---
Gesture recognition for human machine interface |  0.01 |  0.01 |  0.3
Digital twins for manufacturing |  0.01 |  0.01 |  0.5
Traffic monitoring |  0.5 |  0.5 |  0.5
Robots to cobots environment mapping |  0.01 |  0.01 |  0.5
Robots to cobots object sensing |  <0.01 |  <0.01 |  0.1

##  IV Gap Analysis

###  IV-A Positioning Gap Analysis

In order to identify the gap between the requirements for the envisioned 6G
use cases shown in Section III and the capabilities of existing technologies,
we need to establish the achievable performance of state-of-the-art
technologies in localization and sensing. For localization we use 3GPP Rel-16
as the baseline, and for sensing we consider the performance of the
commercially available radar and lidar sensors.

####  IV-A1 Position Accuracy

The positioning accuracy of different positioning methods, namely multi-cell
round-trip-time (multi-RTT), UL-TDoA and DL-TDoA in fifth generation (5G) new
radio (NR) frequency range 1 (FR1) and frequency range 2 (FR2) is shown in
Table II. Simulations are done for 3D-urban macro (UMa), 3D-urban micro (UMi)
and indoor open office (IOO) environments with 50ns synchronization error. It
is seen that the achievable accuracy for 90% of the users with the best method
can be around $1\,\mathrm{m}$ for FR1 and $10\,\mathrm{cm}$ for FR2 in an IOO,
and $3-4\,\mathrm{m}$ with FR1 in UMi and Uma.

TABLE II:  Achievable 5G positioning accuracies (in meters) for different methods and different deployment environments [1]. Cases with coverage limitation are shown as N/A. Method |  UMa |  UMi |  IOO
---|---|---|---
DL-TDoA |  FR1 |  4.37 m |  3.48 m |  2.10 m
FR2 |  N/A |  1.11 m |  0.17 m
UL-TDoA |  FR1 |  35.14 m |  3.88 m |  2.19 m
FR2 |  N/A |  N/A |  0.18 m
Multi-RTT |  FR1 |  30.29 m |  2.99 m |  1.11 m
FR2 |  N/A |  N/A |  0.07 m

5G capabilities for positioning accuracy are shown as vertical lines in Fig.
3. As it is seen from the figure, the required accuracy for the majority of
use cases can not be met for the corresponding deployment scenario. For
example the positioning accuracy that is required for remote healthcare which
is supposed to be available for both indoor and outdoor scenarios can only be
met by 5G indoor positioning capability. Use cases demanding moderate accuracy
($0.1-1$ m) can be supported by 5G positioning methods mainly in IOO-like
scenarios. Finally, the stringent positioning accuracy ($<0.1$ m) requirements
cannot be met in the considered scenarios.

####  IV-A2 Latency

End-to-end positioning latency in 5G depends on the signaling delays between
participating nodes and the positioning method employed. Latency evaluations
for DL-TDOA, UL-TDOA, and multi-RTT are presented in [2], which shows
latencies on the order of $150-300\,\mathrm{ms}$ depending on the positioning
method. Considering the most stringent latency for the 6G use cases, which is
around $10\,\mathrm{ms}$, there is at least one order of magnitude gap between
the state-of-the-art methods and envisioned 6G use cases. 5G capabilities for
positioning latency are shown as diagonal lines in Fig. 3 for different device
speeds.

####  IV-A3 Scalability

5G positioning methods that are based on broadcast signals (e.g. PRS) can be
used by multiple UEs or transmission and reception points for positioning
measurements, given that the nodes performing the measurements are in coverage
of the broadcast signals. Hence, those methods are scalable and can be
practically used to support the scalability requirements of the previously
elaborated use cases. There is, however, an important limitation that will
still be present in 5G. Accurate positioning requires radio resources (e.g.,
PRSs), which would thus occupy communication resources while locating. The
higher the accuracy required, the larger the bandwidth of the channel that
will be unavailable for communication. 6G must solve this well-known problem
by enabling simultaneous communication and positioning.

####  IV-A4 Availability

Future generations of cellular networks need to be designed to obtain seamless
and pervasive connectivity in a variety of different contexts, matching
stringent QoS requirements in outdoor and indoor scenarios with a cost-aware
and resilient infrastructure [6]. The availability of the positioning system
indicates that the position error (PE) is less than a threshold. High path
loss is one of the major issues affecting the availability, which can be
mitigated with the implementation of directional antennas and massive MIMO.
Nevertheless, the angular coverage of antenna arrays (compared with
omnidirectional antennas) will unavoidably be sacrificed. We expect new
techniques such as reconfigurable intelligent surfaces (RIS), distributed MIMO
(D-MIMO), and scene-aware localization and sensing can meet the availability
requirements. Considering rather limited availability of 5G, there is a
significant gap between 5G capabilities and required availability for 6G use
cases as shown by color mapping in Fig. 3, based on reported values in the
technical literature (e.g., [2, 5]), when available. We observe 3 groups of
requirements: use cases with not very stringent requirements, involving asset
and people tracking and context-aware services; use cases involving robots and
vehicles with strict availability requirements, and finally new medical use
cases requiring extreme availability. Considering that the achievable
availability of 5G is 90% [1], the availability for most of the use cases need
to be improved.

####  IV-A5 Orientation Accuracy

The development of radio access technology-based positioning has been done
considering regulatory and IIoT use cases, where UE orientation estimation is
not a primary objective. For example, in regulatory use cases such as
positioning of emergency call originating UE, knowing UE heading is not
critical. This is partially due to the limited angular resolution provided by
small antenna arrays. However, the next generation positioning use cases such
as positioning for AR, positioning for collaborative robots, and local
coverage for temporary usage greatly benefit from orientation estimation. In
this regard, orientation accuracy should also be included in the primary
objectives.

####  IV-A6 Identified Gaps

The position accuracy, latency, and availability gaps between 5G and 6G
systems are summarized in Fig. 3. To support the identified positioning use
cases, future radio access networks must meet the requirements on the above-
mentioned fundamental KPIs. The required positioning and orientation accuracy
can be achieved only when the localization service is available, which is
subject to coverage (indoor or outdoor), network deployment ( geometric
dilution of precision (GDOP) is one of the limiting factors), and
synchronization between the network nodes. As a result, the next-generation
positioning methods must, in principle, be able to address these issues and
should offer more accurate position and orientation estimations by exploiting
potential wide bandwidth and array size. To achieve high scalability
supporting much more devices in the future networks, it is of utmost
importance to utilize the time (PRS allocation), frequency (bandwidth
distribution), and spatial (beamforming and codebook optimization) resources
with the highest efficiency. The latency budget evaluations show that the
latency of 5G positioning methods depends on the employed method and can
support many of the identified use cases. Nonetheless, the newly emerged use
cases (e.g., enhanced remote health care, telesurgery, and collaborative
robots) demanding quasi-real-time positioning mandate new technologies to
obtain a much lower latency than what can be achieved by 5G systems.

###  IV-B Sensing Gap Analysis

In 5G there is so far no support for radio based sensing. To have a baseline
against which to compare the expected performance requirements in 6G, we use
radar and lidar.

####  IV-B1 Legacy solutions

What can be achieved with radar and lidar technologies differs depending on
the application and the environment. To have some numbers for comparison with
the foreseen use cases in 6G, we have chosen Arbe radar, which can achieve a
location accuracy and range resolution of around $0.5\,\mathrm{m}$ at a range
of $300\,\mathrm{m}$ and a velocity resolution of $0.1\,\mathrm{m/s}$ with a
latency of $33\,\mathrm{ms}$ [2]. The example we have chosen for lidar is the
Hesai Pandar64, which can reach a position accuracy of $0.02\,\mathrm{m}$ at
distances in the range $0.5-200\,\mathrm{m}$ with a latency of
$50\,\mathrm{ms}$ and a surface with reflectivity of at least $10\%$ [2]. It
does not provide any velocity estimate though. When comparing these values
with the requirements for the different sensing use cases listed in Table I, a
few things can be noted.

The lidar is good at positioning and is close to satisfying the location
accuracy and range resolution demands of all the listed use cases, except for
the object sensing in robots to cobots. Worth noticing though, is that the
gesture recognition and the object sensing are performed at short ranges,
which decreases the accuracy of the lidar. There is also a possible safety
risk of having lasers pointing at a user from a very short distance as would
be the case in gesture recognition. Another problem for the lidar is the lack
of velocity measurements, meaning that it cannot fulfill the velocity
resolution requirements for any of the use cases.

The radar is much better than the lidar in terms of velocity resolution and
does actually fulfill the requirements for all the use cases. On the other
hand, it lacks the precision in location accuracy and range resolution and
only fulfills the requirements for the traffic monitoring use case. An
advantage of the radar compared to lidar is that it is not as sensitive to the
reflectivity of different materials in the surroundings. Possible health risks
for users are also lower compared to the lidar.

####  IV-B2 Identified Gaps

It is possible to satisfy most of the requirements listed in Table I using
legacy radar and lidar. However, there is only one use case where it is
possible to satisfy all requirements with only one technique, and that is road
traffic monitoring. As was mentioned earlier, there exists a wide range of
different radars and lidars, and in this case, we have only used two different
examples for comparison. There are radars that perform better at positioning
and lidars that give velocity information, but that is then at the cost of
other performance metrics. The bottom line of this gap analysis is that there
is no technique available today which satisfies all the requirements of the
use cases. To provide a fair evaluation of the gap between 6G requirements and
the capabilities of legacy solutions, we consider radar as the baseline
solution. Fig. 4 shows the gap between the most stringent requirements for 6G
use cases and the corresponding capabilities of legacy radar as mentioned
earlier.

Figure 4: Sensing gap between legacy radar (in blue) and 6G (in red).

##  V Opportunities and Challenges for Positioning and Sensing in 6G

###  V-A Radio Enablers

In order to close the gap between 5G capabilities and 6G requirements, various
technical enablers, including high-frequency signals with large bandwidths and
massive arrays, intelligent surfaces, and joint design of multi-functional
hardware/waveforms have been considered. Below these are discussed in detail.

##### High-resolution Sensing with Large Bandwidths and Large Arrays

The resolvability of multipath components in angle, range, and Doppler domains
plays a crucial role in positioning and sensing. Higher carrier frequencies
can accommodate larger bandwidth, resulting in superior range resolution. In
addition, smaller wavelengths can bring significant antenna miniaturization,
thus enabling the deployment of massive antenna arrays and leading to high
angular resolution [7]. This enables high-resolution sensing and mapping
applications without being affected by ambient light and weather conditions,
as opposed to visible light and infrared-based technologies [8].

##### Reconfigurable Intelligent Surfaces

As one of the key enablers in 6G, a reconfigurable intelligent surface (RIS)
can reflect an incoming electromagnetic wave towards a desired direction via
programmable passive reflecting unit cells and a controller, which implies
lower deployment and operational costs than a BS or a relay [9]. Under line-
of-sight (LoS) blockage conditions, RISs can create controllable non-line-of-
sight (NLoS) links to improve coverage and communication quality. In
positioning and sensing applications, RISs with known locations can boost
accuracy by providing additional geometric measurements [10]. Through tailor-
made design of RIS phase shifts (i.e., passive beamforming), positioning and
sensing performance can be enhanced significantly under a-priori knowledge of
UE/target locations.

##### Joint Hardware and Waveform Design

Future positioning and sensing services shall rely on the ubiquitously
available communication network and its hardware, thus avoiding the deployment
of costly parallel infrastructure. Regarding joint waveform design, multi-
carrier communication waveforms, such as orthogonal frequency-division
multiplexing (OFDM), are attractive for positioning and sensing thanks to wide
availability and efficient implementation [11]. On the other hand, single-
carrier waveforms can offer a better solution in terms of hardware efficiency
due to low peak-to-average power ratio (PAPR), but may lead to higher side-
lobe levels than OFDM. To investigate their resolution, accuracy and clutter
rejection characteristics, waveforms (single- or multi-carrier) can be
evaluated through range-Doppler ambiguity function. Due to inherent trade-
offs, the joint waveform optimization for positioning, communications and
sensing requires careful consideration of conflicting requirements, such as
data rate, accuracy, and main-lobe width and side-lobe levels of the ambiguity
function [12]. Moreover, joint communications-sensing waveforms should be
robust to hardware imperfections at high frequencies, necessitating
simultaneous design of multi-functional transceiver hardware and waveforms.

##### Algorithmic Developments

With the high delay/angular resolution in 6G, two promising research threads
for positioning/sensing algorithms arise in a complementary manner. _Model-
based_ algorithms can exploit geometric optics in conjunction with
optimization theory and statistical signal processing [13], while _model-free_
techniques rely on data-driven machine learning [14]. Through their rigorous
mathematical foundations and explainability, model-based methods seem
attractive. Under severe hardware impairments and/or intractable mapping from
measurements to position, data-driven approaches can become highly effective.
In 6G scenarios, algorithms that can harness both data and domain knowledge
will be key to achieving extreme positioning/sensing performance.

Figure 5: 6G radio enablers that can help close the gaps in support of 6G use
cases. Closing the gaps requires addressing two important challenges.

###  V-B Challenges

To fully harness the radio enablers for extreme performance, two fundamental
challenges have been identified: hardware impairments and harsh channel
conditions. In addition to those radio challenges, integrating highly accurate
and low-latency positioning and introducing the totally new feature of
integrated sensing pose further challenges and requirements, for example to
the architecture and service offering concepts.

##### Hardware Limitation and Impairments

Hardware impairments bring more severe effects on positioning and sensing than
on communication. Ranging accuracy degrades mainly due to timing errors,
whereas angle estimation accuracy is degraded by antenna imperfections.
Examples of hardware imperfections include phase noise, mutual coupling,
nonlinear distortion, and frequency-selective impairments [15]. While some of
the hardware impairments can be compensated through calibration (antenna
mutual coupling and linear distortions), others, such as phase noise, must be
compensated dynamically during operation. In order to meet the low latency
demands, a large bandwidth is required, but that is subject to hardware
limitations. In addition, a large volume of data requires more storage and
computational resources that can, in turn, limit the performance improved by
these features.

##### Harsh Propagation Channel

Depending on the relation between the wavelength and the size of objects, the
radio channel can exhibit varying characteristics. At _sub-6 GHz_ , the
channel has a very complex relationship to the environment and small movements
lead to large power fluctuations due to small-scale fading. At _mmW bands_ ,
obstacle penetration is reduced, and reflection and scattering become more
important phenomena. A sparser channel allows for higher multipath
resolvability (characterized by fewer propagation clusters), and larger
bandwidths and large antenna arrays enable accurate positioning. At _100 GHz
and above_ , mainly multipath due to metallic objects will be visible, either
in the form of moving incidence points or virtual anchors, or (groups of)
smaller objects (e.g., pillars), behaving as static points. Additionally, the
Doppler shift will greatly increase at the upper mmW frequency range. At such
frequencies, the channel state changes faster requiring more frequent updates
which demands better spatial consistency of the developed channel models so
that consecutive channel impulse response samples are accumulated over time.
Characterization of the angular, delay, and Doppler spreads due to extended
objects, molecular absorption, link gains, and behavior under mobility are
important challenges to be addressed, e.g., measured evidence and channel
models.

##  VI Outlook for Positioning and Sensing in 6G

In contrast to 5G, 6G will consider positioning and sensing as an integrated
part of the system, with joint waveforms and hardware, as well as important
cross-functional benefits. One of the important foreseen uses of 6G radio will
be to support a wide variety of extremely challenging use cases, not only in
terms of communication requirements, but also for positioning and sensing. The
goal of this paper is to list a selection of these 6G use cases, determine
their positioning and sensing requirements, and perform a gap analysis against
the state-of-the-art. In terms of positioning, we reveal that in three key
KPIs (accuracy, latency, availability), there is a significant gap between the
5G capability and the 6G requirements. In terms of sensing, we similarly found
gaps in certain KPIs (accuracy and resolution) between state-of-the-art
sensors and 6G sensing requirements. To bridge this gap, this paper also
presented a practical view of positioning for 6G radio, considering foreseen
enablers (large bandwidths and arrays, RIS, algorithmic developments) and
challenges (channel model mismatch and hardware impairments).

Next generation mobile networks will feature improved positioning performance,
and will enable sensing the environment. Through this, new services will arise
using such information, which will have other implications. For instance,
certain services will require intermediate information with uncertainty and
integrity guarantees, while other services will have security concerns that
need to be carefully addressed, such as the access rights to different
location services, location jamming, location falsification, as well as issues
related to privacy and secrecy.

Overcoming the challenges while harnessing the enablers in support of 6G use
cases, will constitute a major research endeavor for the coming years.

## Acknowledgments

This work was supported, in part, by the European Commission through the H2020
project Hexa-X (Grant Agreement no. 101015956) and the MSCA-IF grant 888913
(OTFS-RADCOM).

## References

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Ali Behravan is a master researcher at Ericsson research, Sweden, working in
the area of wireless communication and sensing.
---
Vijaya Yajnanarayana is a master researcher at Ericsson Research, India,
working in the area of radio signal processing and artificial intelligence.
---
Musa Furkan Keskin is a researcher and Marie Skłodowska-Curie Fellow (MSCA-IF)
at Chalmers University of Technology, Sweden, working on joint radar-
communications and mmWave positioning.
---
Hui Chen is a postdoctoral researcher at Chalmers University of Technology,
Sweden, working on 5G/6G localization and sensing.
---
Deep Shrestha is a senior researcher at Ericsson research, Sweden, working on
radio localization and sensing.
---
Traian E. Abrudan is a senior researcher at Nokia Bell Labs, Finland, working
on wireless communication and localization.
---
Tommy Svensson is Professor at Chalmers University of Technology, Sweden,
leading wireless systems research area with a focus on air interface and
wireless backhaul/ fronthaul technologies for future wireless systems.
---
Kim Schindhelm is a research scientist at Siemens’ corporate research unit in
Munich, Germany, working on localization.
---
Andreas Wolfgang is CTO at Qamcom Research & Technology, Sweden, working with
communication and sensing.
---
Simon Lindberg is a senior system developer at Qamcom Research & Technology,
Sweden, working with communication and sensing.
---
Henk Wymeersch is Professor at Chalmers University of Technology, Sweden,
working on radio localization and sensing.
---
  *[3GPP]: the third generation partnership project
  *[6G]: sixth generation
  *[BSs]: base station
  *[UE]: user equipment
  *[TDoA]: time-difference-of-arrival
  *[multi-RTT]: multi-cell round-trip-time
  *[5G]: fifth generation
  *[NR]: new radio
  *[FR1]: frequency range 1
  *[FR2]: frequency range 2
  *[UMa]: 3D-urban macro
  *[UMi]: 3D-urban micro
  *[IOO]: indoor open office
  *[GDOP]: geometric dilution of precision
  *[RIS]: reconfigurable intelligent surface
  *[OFDM]: orthogonal frequency-division multiplexing