6G White Paper on Machine Learning in Wireless Communication Networks

Samad Ali111Centre for Wireless Communications, University of Oulu, Finland,
(emails:{samad.ali, nandana.rajatheva, hamid.shiri, kai.mei}@oulu.fi)., Walid
Saad222 Wireless@VT, Bradley Department of Electrical and Computer
Engineering, Virginia Tech, USA, (email: walids@vt.edu)., Nandana
Rajathevacwc, Kapseok Chang333Electronics and Telecommunications Research
Institute (ETRI), Daejeon, South Korea (email: kschang@etri.re.kr)., Daniel
Steinbach444InterDigital, Inc., USA, (email:
daniel.steinbach@interdigital.com), Benjamin Sliwa555TU Dortmund University,
Germany, (emails: {benjamin.sliwa, christian.wietfeld}@tu-dortmund.de),
Christian Wietfeldtudortmund, Kai Meicwc, Hamid Shiricwc, Hans-Jürgen
Zepernick666 Blekinge Institute of Technology, Sweden, (emails: {hans-
jurgen.zepernickm, thi.my.chinh.chu}@bth.se), Thi My Chinh Chubk, Ijaz
Ahmad777VTT Technical Research Center, Finland, (emails: {ijaz.ahmad,
jyrki.huusko}@vtt.fi), Jykri Huuskovtt, Jaakko Suutala888Biomimetics and
Intelligent Systems (BISG), University of Oulu, Finland, (email:
jaakko.suutala@oulu.fi), Shubhangi Bhadauria999Fraunhofer Institute for
Integrated Circuits IIS, Germany, (email:
shubhangi.bhadauria@iis.fraunhofer.de), Vimal Bhatia101010 IIT Indore, India,
(emai: vbhatia@iiti.ac.in), Rangeet Mitra111111University of Quebec, Montreal,
Canada, (email:rangeet.mitra.1@ens.etsmtl.ca), Saidhiraj Amuru121212IIT
Hyderabad, India, (emai: asaidhiraj@ee.iith.ac.in), Robert
Abbas131313Macquarie University, Australia, (email: robert.abbas@mq.edu.au),
Baohua Shao141414Warwick Institute for the Science of Cities, UK, (email:
b.shao@warwick.ac.uk), Michele Capobianco151515Capobianco - Business
Innovation Management, Pordenone, Italy, (email: michele@capobianco.net),
Guanghui Yu161616ZTE Corporation, China<EMAIL_ADDRESS>,
Maelick Claes171717 Empirical Software Engineering in Software, Systems and
Services (M3S), University of Oulu, (emails: {maelick.claes,
teemu.karvonen}@oulu.fi), Teemu Karvonenoulusfotware, Mingzhe
Chen181818Princeton Univeristy, USA, (email: mingzhec@princeton.edu), Maksym
Girnyk191919Ericson Research, Sweden, (email: maksym.girnyk@ericsson.com),
Hassan Malik202020Prontominds OÜ, Estonia, (email:
<EMAIL_ADDRESS>
2020footnotetext: This paper is a compilation of ideas presented by various
entities at the 6G Wireless Summit 2020. It does not reflect an agreement on
all the included technology aspects by all the involved entities.

###### Contents

  1. 1 Abstract
  2. 2 Introduction
  3. 3 Machine Learning Overview
    1. 3.1 Deep learning
    2. 3.2 Probabilistic methods
    3. 3.3 Reproducing Kernel Hilbert Space (RKHS)
    4. 3.4 Federated Learning
    5. 3.5 Reinforcement Learning
  4. 4 ML at the Physical Layer
    1. 4.1 Research Areas
      1. 4.1.1 Channel Coding
      2. 4.1.2 Synchronization
      3. 4.1.3 Positioning
      4. 4.1.4 Channel Estimation
      5. 4.1.5 Beamforming
      6. 4.1.6 Physical Layer Optimization with ML
    2. 4.2 Implementation of ML At the Physical Layer
    3. 4.3 Future Directions
  5. 5 ML at the Medium Access Control Layer
    1. 5.1 FL for Orientation and Mobility Prediction in Wireless Virtual Reality Networks
    2. 5.2 Predictive Resource Allocation in Machine-Type Communications
    3. 5.3 Predictive Power Management
    4. 5.4 Asymmetric Traffic Accommodation
  6. 6 ML for Security of Wireless Networks
    1. 6.1 The Road Towards 6G Security
    2. 6.2 Wireless Security
  7. 7 ML at the Application Layer
    1. 7.1 ML for 6G Network Performance Management Automation
    2. 7.2 ML-Aided UAV Control
    3. 7.3 Opportunistic Data Transfer in Vehicular Networks
    4. 7.4 Software Development Aspects
  8. 8 Standardization Activities

##  1 Abstract

The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as self-
driving vehicles and voice assistants. Such innovation is possible as a result
of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application
layer. Zero-touch optimization of wireless networks using ML is another
interesting aspect that is discussed in this paper. Finally, at the end of
each section, important research questions that the section aims to answer are
presented.

##  2 Introduction

Today’s technological aspirations will represent tomorrow’s reality with
technologies such as holographic telepresence, eHealth and wellness
applications, pervasive connectivity in smart environments, industry 4.0 and
massive robotics, massive unmanned mobility in three dimensions, augmented
reality (AR) and virtual reality (VR) to name a few. Each of them is expected
to require more effective and efficient wireless communications than ever
before and 6G wireless networks must provide broadband, near-instant, and
reliable connectivity to enable massive data exchange at different frequencies
and by using a large variety of technologies. Moreover, the evolution of
technologies are towards more intelligent device in the internet of things
(IoT) which will require a more reliable, efficient, resilient and secure
connectivity. When the connected objects are more intelligent it becomes
difficult deal with their complexity by using the communication network in a
static, simplistic and rigid manner. The same need will likely emerge for
other “traditional” services such as phone calls or a video streaming, where
the wireless communication network will no longer just provide a connection
between two or more people, but will bring the need to properly authenticate
both parties, guarantee the security of data fluxes and recognizing possible
abnormal behaviors and events. Data exchange will be, in practice, much more
than just pure data exchange and will become the exchange of information,
knowledge, experience, and also past, present, and possibly future properties
of the data. What we can easily anticipate is the fact that larger and larger
amounts of data will be transferred through the future wireless communication
networks and more added value applications and services will heavily depend on
such data exchanges. Machine learning (ML) will represent a basic
functionality to guarantee the efficiency of future wireless communication
networks and, at the same time, can represent the enabling technology for
several added-value applications and services. ML on the wireless
communication nodes can enable several advanced services and quality of
service functionalities for the proposed applications.

Current wireless networks heavily rely on mathematical models that define the
structure of the communication system. Such mathematical models often do not
present the systems accurately. Moreover, there are no mathematical models for
some of the building blocks of wireless networks and devices and as a result,
modeling of such blocks becomes challenging. On the other hand, the
optimization of wireless networks also requires heavy mathematical solutions
that are often not efficient in terms of computational time and complexity,
and, also consume a lot of energy. The above mentioned mathematical models and
solutions will most likely fall short in improving the capacity and
performance of wireless networks that are expected to meet the stringent
requirements that will be set by 6G applications [1]. ML, therefore, will play
a crucial role in 6G wireless networks as it is capable of modeling systems
that can not be presented by a mathematical equation. Moreover, it is expected
that ML tools can be used to replace heuristic or brute-force algorithms to
optimize certain localized tasks. Meanwhile, it is envisioned that ML will
enable real-time analysis and automated zero touch operation and control in 6G
networks. Such intelligence will rely on the availability of data timely
streamed from wireless devices, especially in extreme applications, such as
real-time video monitoring and extended reality (XR). To fully leverage these
capabilities, the network should support ML-native agents that can be freely
placed and moved to the required network locations.

Furthermore, additional ML actions or predictions could be performed by mobile
devices and reported to the network to assist in decision making in resource
management, making mobile devices an integral part of the infrastructure
resource. 6G networks are expected to employ ML agents for multiple functions,
including optimization of the radio interface, adaptive beamforming
strategies, network management, and orchestration. Such functionality will
require data from different domains and sources in the network. This poses
additional requirements on the efficiency of data transfer to avoid the
transmission and storage of massive amounts of data that may never be utilized
over network management interfaces.

ML algorithms should be deployed and trained at different levels of the
network: management layer, core, radio base stations, and as well as in mobile
devices, possibly with the assistance of the network itself (e.g., via
configuration and/or device programmability). These new paradigms may drive
the need for ML-native and data-driven network architecture, as network
functions in the network and management domains may require data from
different sources. Meanwhile, physical-layer algorithms (e.g., link
adaptation), as well as higher layer algorithms (e.g., mobility) can be
optimized with ML agents deployed in a controlled and predictable manner.
Currently, such algorithms tend to be deployed statically, whereas allowing
them to change dynamically would open up for enhanced performance and
utilization. Moreover, allowing also configurations of the network to be
automatized reduces the need for expensive hands-on human work.

The white paper provides a vision for the role of ML in wireless
communications by discussing the various network problems that can utilize
learning methods. A detailed look at the problems at different layers of the
communications protocol stack is provided. Moreover, ML in security of
wireless networks as well as standardization activities are also discussed.

Figure 1: The role of ML in 6G networks.

##  3 Machine Learning Overview

ML models are computing systems that are used to learn the characteristics of
a system that can not be presented by an explicit mathematical model. These
models are used in tasks such as classification, regression, and interactions
of an intelligent agent with an environment. Once a model learns the
characteristics of a system, which is known as a trained model, then it can
efficiently perform the task using some basic arithmetic calculations. ML
spans three paradigms which are known as a) supervised learning: where the
model is learned by presenting input samples and their associated outputs, b)
unsupervised learning, in which, there are no output labels and the model
learns to classify samples of the input, and, c) reinforcement learning, where
an agent interacts with an environment and learns to map any input to an
action. A general overview of some of ML methods is provided in the following.

###  3.1 Deep learning

Deep learning methods based on artificial neural networks (ANNs) have recently
been able to solve many learning problems. The rise of the deep learning
paradigm has mainly been fueled by the availability of sufficient
computational power and access to large datasets. There exist many
architectures in deep learning that are used for various tasks. In this
section, we mention some of the most important deep learning architectures
that are suitable for problems in wireless communications. Multi-layer
perceptrons (MLPs) are the basic models that are generally used in many
learning tasks. Convolutional neural networks (CNN) which use convolution
operation to reduce the input size are often used in image recognition tasks.
For learning tasks which require sequential models, recurrent neural network
(RNN) are most suitable. Autoencoder based deep learning models are used for
dimension reduction and generative adversarial networks (GANs) are used to
generate samples similar to the available dataset.

In the wireless communications domain, the amount of training data is still
far away from being comparable to the huge data sets used by the big industry
players for core applications of deep learning such as computer vision and
speech recognition. Due to the curse of dimensionality [2], deep learning
models require very large training data sets in order to achieve significant
performance increases in comparison to simpler models. Another limiting factor
is the network heterogeneity implied by the coexistence of different mobile
network operators. Although standardized data sets formats could help to
establish interoperability, implementations for network management functions
might differ significantly between the different operators. Moreover, due to
business-oriented principles, operators might rather keep their acquired data
confidentially. These factors leads to the conclusion that deep learning alone
will not be the optimal solution for all data analysis tasks within 6G
networks. Instead, a variety of application- and platform-dependent models
will be required in order to enable cognitive decision making even on highly
resource-constrained platforms such as ultra low power microcontrollers.

###  3.2 Probabilistic methods

There have been a lot of recent advances in probabilistic ML and Bayesian
inference [3] which could potentially be used in 6G wireless networks.
Compared to more classical frequentist methods, they provide probability
theory-based fundamental framework to handle prior knowledge and to quantify
uncertainty, needed in noisy real-world learning and modeling scenarios such
as data rich 6G applications and services. Especially flexibility of handling
small, limited or incrementally growing datasets, non-parametric Bayesian
methods such as Gaussian processes can provide promising interpretable
techniques to model complex spatio-temporal and high-dimensional sensing and
prediction problems in 6G networks. As non-parametric models grow on data, the
computational complexity of these models is the biggest disadvantage compared
to parametric models. However, there has been a lot of work based on
approximation methods such as variational Bayes, Expectation Propagation, and
sampling approaches based on Markov chain Monte Carlo to be able scale these
techniques to distributed big data challenges of wireless communication
systems.

###  3.3 Reproducing Kernel Hilbert Space (RKHS)

Methods Massive connectivity requirements in 6G will result in high
interference, which will culminate in a significant performance-bottleneck.
Furthermore, the massive connectivity will also involve serving a wide range
of devices of various manufacturing qualities and hence will result in
introduction of impairments due to diverse artefacts introduced by non-ideal
hardware (nonlinear characteristics, I/Q imbalance and others), high-mobility
especially in the context of varied industry verticals where a fixed solution
may not be applicable. To fulfill the promise of 10-100 times data-rate
improvement in these scenarios as compared to 5G, the Reproducing kernel
Hilbert space (RKHS) based solutions are particularly useful, as RKHS based
methods are computationally simple, scalable, and have significantly lower
approximation error (even in high-interference non-Gaussian environments that
will be potentially encountered in 6G) as compared to contemporary polynomial
filtering based approaches. Recently, the RKHS based approaches have emerged
as a panacea for mitigation of a variety of impairments in the context of
several applications in the next-generation communication systems. As a
consequence, several RKHS based methods have been proposed for problems such
as detection, tracking and localization [4, 5].

Over the last few years, we have also seen tremendous research in deep
learning being heavily used in wireless communications problems; though there
is a well-known concern regarding the sensitivity of deep learning based
approaches to hyperparameters. Being an active area of research, recent
advances further improves performance of RKHS based approaches by extracting
features using Monte-Carlo sampling of RKHS, and utilizing these features as
input to the deep-learning based approaches, to enhance the performance of
models used in 6G. In addition, RKHS based deep-learning approaches are found
to deliver improved performance as compared to classical deep-learning
algorithms as these features are intrinsically regularized, and are supported
with strong analytical framework. Lastly, the RKHS based solutions have fewer
hyperparameters to tune in general, and there exist several “rules of thumb”
for learning these hyperparameters.

###  3.4 Federated Learning

Traditional centralized ML algorithms [6] require mobile devices to transmit
their collected to the data center for training purpose. Due to privacy issues
and communication over, it is impractical for all wireless mobile devices to
transmit their local data for training ML models. Federated Learning (FL) is a
distributed ML algorithm that enables mobile devices to collaboratively learn
a shared ML model without data exchange among mobile devices. In FL, each
mobile device and the data center will have their own ML models. The ML model
of each mobile device is called local FL model while the ML model of the data
center is called global FL model. The ML model that is The training process of
FL can be summarized as follows:

  * a.

Each mobile device uses its collected data to train its local FL model and
sends the trained local FL model to the data center.

  * b.

The data center integrates the local FL models to generated the global FL
model and broadcasts it back to all mobile devices.

  * c.

Steps b. and c. are repeated until find the optimal FL models to minimize the
FL loss functions.

From the FL training process, we can see that mobile devices must transmit the
training parameters over wireless links. Hence, imperfect wireless
transmission, dynamic wireless channels, and limited wireless resource (e.g.,
bandwidth) will significantly affect the performance of FL. In consequence, a
number of the existing works such as [7] and [8] have studied the optimization
of wireless networks for the implementation of FL. Meanwhile, since FL enables
mobile devices to collaboratively train a shared ML model without data
transmission, it has been studied for solving wireless communication problems
such as intrusion detection [9], orientation and mobility prediction, and
extreme event prediction.

###  3.5 Reinforcement Learning

In a reinforcement learning problem, an agent interacts with an environment
and learns how to take actions. At every step of the learning process, the
agent observes the state of the environment, takes action from the set of
available actions, receives a numeric reward, and moves to the next state. The
agent aims to maximize long term cumulative reward. Many wireless problems
such as resource allocation can be formulated as a reinforcement learning
problem. Neural networks can be used in reinforcement learning problems as
function approximators to learn the rewards that are generated by the
environment or values of each state. Various deep reinforcement learning
architectures can be used to solve many problems in wireless networks such as
power control, beamforming, and modulation and coding scheme selection. One
major limitation of RL is its high reliance on training. However, there has
been some recent advances towards reducing this reliance, particularly when
dealing with extreme network situations. In particular, the concept of
_experienced deep reinforcement learning_ was proposed in [10] in which RL is
trained using GANs that generate synthetic data to complement a limited,
existing real dataset. This work has shown that, by gaining experience, deep
RL can be come more reliable to extreme events and faster to recover and
convergence.

In this section, we have tried to answer the following research questions:

  * •

Which ML methods will play a major role in 6G wireless networks?

  * •

Which areas of 6G wireless networks will use deep learning?

  * •

Why deep reinforcement learning will be one of the major components of
automation of 6G wireless networks?

  * •

How can the goal for open data access be brought together with business-
oriented mobile network operator interests?

  * •

How can models be efficiently transferred to highly resource-constrained
platforms?

  * •

How to dynamically select and deploy application- and platform-dependent
models?

##  4 ML at the Physical Layer

In recent years, ML has begun to penetrate into all walks of life, including
the field of wireless communication. The physical layer of traditional
wireless communication is generally designed based on mathematical models, and
several major modules are modeled and optimized separately. This design method
can adapt to the fast time-varying characteristics of the physical layer, but
often some nonlinear factors in the physical layer cannot be modeled. The
research and attempt to use ML in the physical layer of wireless communication
have been carried out in recent years [11], and some progress has been made,
so it is necessary to integrate ML into the physical layer of 6G wireless
systems. There are several levels to integrate ML into 6G wireless
communication system. The first level is ML for some special functions. We
should first consider using ML to replace some functions that are not well
solved at present. For example, interference detection and mitigation, uplink
and downlink reciprocity in FDD, channel prediction and so on. These problems
still exist in the current communication systems due to lack of accurate
models or non-linearity. The second level is to update the existing discrete
modules. The traditional design of each module is generally based on a linear
model, so once the system encounters strong non-linear factors, the
performance of the system will decline sharply. The third level is the joint
optimization of the modules in the physical layer. As mentioned above, the
traditional design of physical layer is divided into modules and optimized
separately. For example, decoding, modulation and waveform are designed
separately in the traditional design. Once the three are considered together,
the complexity of the receiver is often too high to be optimized as a whole.
However, for ML, it is not necessary to design all kinds of coding schemes
carefully, nor to think about all kinds of constellations. The best end-to-end
mapping mode can be obtained automatically by learning. Which modules in the
physical layer use ML for joint optimization is a future direction to be worth
exploring. The fourth level is the integration of ML and the existing model
based methods. Although the traditional model-based method is sometimes over
idealized, it can describe the main characteristics of a process after all. If
some existing model features are used for reference in the design process of
ML and input into ML as additional information, it is likely to overcome some
inherent defects of ML, such as the need for huge training data, under fitting
or over fitting, slow convergence, etc.

The above discussion provides an overview of how ML will be used in the
physical layer of the communication systems. In the following, we provide a
detailed view of some of the problems in the physical layer that can benefit
from ML methods.

###  4.1 Research Areas

Some of the major research areas of ML-driven PHY-layer include channel
coding, synchronization, positioning, and channel estimations. Coping with
these items, this section defines their definitions, and provides their
technical trends and prospects.

####  4.1.1 Channel Coding

Channel coding is needed to overcome wireless channel imperfections or to
correct errors that may occur on the channel. Since the discovery of the Turbo
code, which is close to Shannon limit, channel coding codes such as low-
density parity-check (LDPC) and Polar codes have been actively studied until
recently. The research direction of recent channel codes has been moving
towards enabling a rapid coding and decoding process to support low-latency
services, while also having a high-fidelity error correction capability. To
solve complex problems of channel coding, studies have been underway to apply
deep learning to channel coding [12], [13]. To replace the channel coding
portion of the communication system with deep learning, learning is required
for codeword length equal to at least hundreds of bits (control channel
assumption) and the resulting output so far remains tens of bits (16 or 32 by
Polar Code). In other words, it is difficult to compare/predict whether the
code length actually can be learned, and how much of the benefits will be in
terms of computational complexity and time compared to the currently
commercialized state-of-the-art. The difficulty of increasing code length is
that the range of codes to learn increases exponentially as the length of code
increases (e.g., to learn length-$n$ codeword, the number of $2^{n}$ cases
must be learned). There are several attempts to overcome this problem (e.g.,
how to learn about all zero-codeword), but there are no clear findings yet.
However, in order to be applied to actual systems, it should be reviewed not
only for performance but also for the time spent in decoding and other
aspects. For example, it is necessary to graph an optimal scheme and a sub-
optimal scheme in terms of both performance and time. In addition, these
graphs may vary not only over time but also by a service using channel coding
or by parameters that a system considers important (such as computational
complexity).

####  4.1.2 Synchronization

In general, all devices must go through time/frequency and cell
synchronization procedure without exception, so synchronization that meets
system requirements is the starting point for all standards, including 4G
long-term evolution (LTE) and 5G new radio (NR). Accordingly, it is pivotal to
have synchronization technology that meets system requirement on
synchronization accuracy, even in the worst radio channel environment, the
fastest mobile environment, and the highest carrier frequency offset (CFO)
environment. An end-to-end auto-encoder (AE) based communication system is
likely to achieve global optimal performance, with the possibility of
implementing the communication system as an end-to-end deep neural network,
including transmitter, channel model, synchronization using sync signal (SS)
as reference, and receiver [11]. However, in the presence of sampling timing
offset (STO) and sampling frequency offset (SFO) between transmitter and
receiver, it is still too early to perform both synchronization and signal
detection/decoding with only one auto-encoder (AE) based deep learning neural
network. Recent research has shown that deep learning technologies using SS
separately from signal detection/decoding [14],[15], forward error correction
(FEC) based synchronization [16], and classification based synchronization
[17] were introduced.

####  4.1.3 Positioning

Current mobile positioning technology has identified the location of users in
indoor or outdoor environments based on various signals received from mobile
devices or wireless channels using a mathematical approach. However, a fatal
problem with the mathematical approach is that if there are non-line-of-sight
(NLoS) multi-paths, there are high positional errors. As a means of solving
this problem, most of recent ML methods have been deep neural networks. To
date, the deep learning technology applied to the position technology is
mostly based on indoor dimensions, and existing fingerprint methods are
characterized by learning from deep learning model and applying it. Received
signal strength (RSS), channel state information (CSI), or Hybrid information
are used as input data for the fingerprint based deep learning. The learning
and experiment results of most deep learning-based positioning technologies
were used to build learning data and measure performance in ideal
environments, such as fixed experimental environments. There is no guarantee
that deep learning models, which represent the best performance in space A,
will also perform well in space B. Therefore, it is necessary to develop a
learning model that is less sensitive to changes in the environment and
represents good performance, or to make the best learning model for each
environment. In real-world environments, input data may not be as perfect as
they were in learning (e.g., RSS information missing, if the bulb is turned
off when using a light sensor, temperature changes, environmental changes by
people/things not considered, etc.). Therefore, it is necessary to develop and
analyze a learning network model that can operate when the input data value
changes. Most positioning systems have been carried out taking into account
only one target environment. However, it is expected that the actual system
will have interference effects in an environment with multiple people or
multiple devices. Therefore, the performance difference of given deep
learning-based location-level technology between the experimental and the
actual environment must be analyzed in the course of the research. Through
this analysis, a deep learning technology with large positioning difference
should facilitate evolution by means of adaptive technology (e.g., combined
with signal processing technology) to adapt to the actual environment. On the
other hand, a deep learning technology without significant positioning
difference in itself should facilitate evolution through coupling with online
learning in addition to offline learning.

####  4.1.4 Channel Estimation

In many communications standards including LTE and 5G NR, channel estimation
is an inevitable module, which provides the information about how the channel
distorts the transmitted signal. Linear minimum mean square error (LMMSE)
estimation has the optimal performance under the condition that the channel is
linear and stationary. But real channels may be non-linear and non-stationary.
Under such complicated channel conditions, the analytical form of the optimal
estimator is hard to be derived. On the other hand, deep learning based
channel estimation can be optimized through the training of the neural network
despite the complicated channel environments. Moreover, channel estimation
along with other modules, e.g., equalization [18], can be realized in a single
DNN. Hence, the separate modules in conventional communication systems can be
jointly optimized to achieve better performance. Nevertheless, the existing
deep learning based channel estimation techniques have one common shortcoming.
Since DNN has to be trained offline because of requirements on long training
period and large training data, mismatches between the real channels and the
channels in the training phase may cause performance degradation. In the
future researches, online training and constructing training data that matches
real-world channel might be promising approaches to overcome this problem.

####  4.1.5 Beamforming

At the physical level, _intelligent_ beamforming and smart antenna solutions
can also greatly contribute at guaranteeing performance, stability of the
throughput, reduce sensitivity to interferences, extend coverage, enable
highly mobile applications, and reduce energy consumption. We already
witnessed the evolution of antenna technologies from relatively _dumb_ and
basic antennas to more advanced _active_ antennas that include progressively
more and more _intelligence_ to map the knowledge of the _environment_ and
guarantee the optimization of the radio links. This evolution is already an
integral part of 5G communication and will be boosted further with 6G
communication where all elements in the communication chain will be
characterized by some level of intelligence or at least capacity to operate in
a optimal manner following some degree of training. Again at this level ML
(and more specifically deep learning) can represent the optimal solution to
support adaptive and real time massive MIMO beamforming, follow mobility
patterns to capture structural information of the radio channels, coordinate
beams with neighbor base stations, properly allocate power, adapt emission
patters for mobile devices, exploit beamforming for added value services.
Dedicated hardware, other than dedicated algorithms, can help implement
efficient machine learning solution to support a new generation of intelligent
beamforming and smart antennas.

####  4.1.6 Physical Layer Optimization with ML

At the physical layer, many optimization problems are non-convex, e.g.,
maximizing throughput by means of power control, multiuser spectrum
optimization in multicarrier systems, optimization of spectrum sensing for
cognitive radios, optimal beamforming formulated as a sum rate maximization
problem under a total power constraint to name only a few. This type of
problems may be solved using dual decomposition techniques that require
iterative algorithms which in turn often cannot be computed in real time due
to high computational load. To alleviate the high computational complexity and
resulting latency associated with existing iterative algorithms, heuristic
solutions have been proposed for some physical layer problems such as
beamforming design. Although heuristic solutions can be obtained with low
computational delay, this benefit comes at the expense of performance loss. On
the other hand, deep learning techniques have great potential to find
solutions to those problems in real time while maintaining good performance
and reducing computational delay. As such, deep learning is a powerful
technique for designing, enhancing, and optimizing one or multiple functions
in the physical layer for 6G. This includes CNNs for signal classification and
DNNs for channel estimation and signal detection.

Recent research on physical layer optimization that exploit ML includes a deep
learning framework for optimization of multi-input multi-output downlink
beamforming [19]. The CNN-based solution takes expert knowledge into account
such as uplink-downlink duality as well as the known structure of the optimal
solutions. The proposed beamforming neural network (BNN) is shown to achieve a
good trade-off between performance and computational complexity. Open
questions in this context include providing solutions for imperfect CSI and
multi-cell scenarios.

In case that joint optimization of functional blocks at the physical layer is
considered and the channels are too complex for modeling, deep learning models
are the best solutions for achieving performance improvement. Conventionally,
the channel estimation based on the pilot estimation and the signal detection
based on channel estimation are executed separately one after the other. In
[20], by considering the channel as a black box, a fully connected DNN with
five layers is implemented for joint channel estimation and detection. The
received signals corresponding to both the transmit signals and the pilots are
taken as inputs of the DNN to recover the transmit signals as outputs. This
DNN has been shown to be more robust to the number of pilots than conventional
methods and is able to address complicated channel distortions. Future
directions in physical layer optimization with ML center around the paradigm
of an autoencoder that has been introduced in [21] aiming at a deep learning-
based end-to-end physical layer architecture. In this approach, transmitter
and receiver components are jointly optimized in the presence of a given
channel. Autoencoders of the deep learning network for building the end-to-end
physical layer modules consider designing a communication system as an end-to-
end reconstruction optimization task. The autoencoder would jointly learn
transmitter and receiver realizations without the need of expert knowledge and
modules. Given the complexity related to building end-to-end physical layers,
it is currently more feasible to exploit deep learning techniques for
designing, enhancing, and optimizing one or multiple functions in the physical
layer for 6G.

###  4.2 Implementation of ML At the Physical Layer

While power, cost and size are always considerations in implementations of
neural networks, they are of extreme importance in implementing ML algorithms
in user equipment (UE) or at the cell edge. Additional considerations during
simulation and prototyping of ML in UE devices need to be taken into account
to optimize physical realization of designs. Implementations may be software-
centric early in the design-phase, the only way to achieve expected battery
life while processing real-time data is to migrate to a HW centric solution.
The following sections outline the three main phases of development and the
expected requirements of an artificial neural network (ANN) during those
stages. It is expected that training will occur during the simulation and
prototype phases. For a final product, the feed-forward network will be ported
to the physical device where weights are still software defined but other
features may be fixed in the hardware design.

#### Simulation

The first stage of development of a wireless modem typically is a software
simulation of the physical layer transmitter and receiver. The air interface
is simulated with a channel model that tries to recreate real world conditions
such as noise, fading, multipath, Doppler spread and path loss. Various
aspects of the receiver can be implemented in an ANN which are talked about in
this paper. At this point, ML will take place in ANNs where the number of
nodes, layers, connections, activation functions and back propagation loss
functions all need to be flexible while the network trains. During this
initial stage, the many parameters and characteristics of the ANN will need to
be identified with trade-offs between performance and physical resources. Even
though training of an ANN is not performed in real-time, performance
considerations are still important since there are practical limitations how
long simulations can run. Offloading ML algorithms from a Central Processing
Unit (CPU) to a Graphics Processing Unit (GPU) can increase performance by 10
to 100 times [22]. In addition specific ANN accelerators can improve
performance even more but are not always suited to supporting back-propagation
required for training[23].

In order to train an ANN, many different channel models need to be employed
and run in a Monte-Carlo style simulation with multiple trials. Each trial run
with a different random seed can be fairly complex to generate and take hours
to run since the model simulates impairments at the symbol rate. How well the
ANN will model real world conditions depends upon the quality and diversity of
the channel models. For illustrative purposes, if we have 30 channel models,
each is run 20 times with randomized data and the simulation takes 8 hours to
run that would result in 200 days of run time. This shows that these
simulations would need to run in parallel on a high end grid or cloud based
engine. Also it is obvious that we want to reduce simulation time by
offloading the ANN to specialized hardware. One big task during simulation is
to identify the structure and features of the neural network. If we want to
compare performance of several activation functions or vary the numbers of
connected nodes in each layer, we can see that the computing resources
required in the simulation stage is vast.

A big part of the design with any ML algorithm in the physical layer is to
determine what the inputs to the ANN are. Filtered outputs such as channel
estimator data, FFT output, pilot symbols or possible uniquely filtered data
are all candidates as inputs to the ANN. Raw I/Q samples would likely
overwhelm any reasonably sized ANN and cause convergence to take way too long
if at all possible. Hooks into the processing stack are required to bring out
any raw data that is required as input to the ANN. Also outputs such as BLER,
BER, SINR and CRC validation will need to be fed back into the loss function.

#### Prototyping

After simulation, a prototype platform typically will be developed utilizing a
Field Programmable Gate Array (FPGA) as the main processing engine [24]. It is
desirable to be able to run the platform real-time or at least at a scaled
down rate such as 1/2 or 1/4 of the real-time sampling rate. We want to be
able to transmit and receive over the air in the band of interest so that we
are not limited to training with predefined channel models as in the
simulation stage. In this case, ANNs can be trained over a wide set of
conditions that include varying distance, rural or urban environments, speed
and weather. It is important to be careful so that when training in one
scenario, the ANN doesn’t ”forget” previous scenarios. For example the system
may adapt well to a rural environment but after then training in an urban
environment, the performance in a rural environment may suffer [25].

There are IP cores that can be synthesized into an FPGA to implement a DNN
[26]. These cores such as Xilinx’s Deep Learning Processor Unit (DPU) are
highly configurable allowing the user to allocate resources such as DSP
slices, block RAM, UltraRAM, and convolutional architecture. However these
settings only allow choosing from a fixed set of possible architectures so an
extremely efficient design to fit just what is required is not possible. Also
there are now chips such as the Xilinx Versal [27] where there are up to 400
inference engines built into the FPGA. This will allow for a lot of
flexibility and speed in the design.

There is also an open-source framework for accelerating Deep Neural Networks
on FPGAs called DnnWeaver (dnnweaver.org). The framework lets a developer
specify the ANN architecture from a high level and then the tool automatically
generates Verilog code. It is also platform independent so it is not specific
to one manufacturer over another.

With the end goal of an efficient ASIC, after acceptable performance is found,
the ANN has to be analyzed for optimization. It has been shown [23] that
reducing the number of bits in fixed point multipliers, even from 32 bits to
16 can result in only a very small performance loss but use almost 1/8th the
power and area of the die. Even quantization to 8 bits can result in little
inference loss [28]. Weights that are close to zero can be pruned so that
memory is saved in addition to computational resources as shown in [28] with
minimum accuracy loss. The assumption is that the number of nodes and layers
in an ANN would not change significantly when porting the design to an
Application Specific Integrated Circuit (ASIC).

#### Product Phase

Any final product with an ANN to facilitate physical layer processing will
have to place hard limits on the number of nodes, layers and bits in fixed
point MAC operations. Once the design is ported to an ASIC, it will be assumed
that a fully trained ANN will be imported to the design. However there has to
still be some flexibility in updating the network as well so that weights and
some connection information can be updated through software downloads.

Design considerations have to be made regarding which inputs and outputs will
be available to/from the ANN. Allowing the ANN to reside on a separate co-
processor, requiring moving data off chip can take up more than the available
timeline. Any ANN would have to be treated the same as any physical layer
processing block where the data is readily available and the neural net is
part of the processing chain.

###  4.3 Future Directions

It is expected that deep learning technology will be employed for wireless
transmission of 6G mobile communication infrastructure in the next 10 years by
carrying out practical learning online as part of the model trimming approach
to overcome differences in performance based on learned wireless channel model
and actual wireless channel environment.

Figure 2: Impact and uncertainty regarding deep learning-driven PHY-layer
technologies.

More specifically, firstly, we predict impact and uncertainty regarding deep
learning-driven PHY-layer technologies as shown in Fig. 2. The classification
criteria in this figure are as follows. Performance degradation due to offline
learning and actual mismatch in the wireless channel environment is expected
to be relatively higher for positioning items. Because channel coding assumes
radio channel compensation, the effect of radio channels is reduced in the
form of colored noise. And synchronization is not to perform any channel
estimation itself but to perform a correlation greater than the
synchronization signal length for a given radio channel, which may be less
affected by environmental inconsistencies. On the other hand, positioning is
based on the fact that it is directly related to the nature of radio channels
and therefore more likely to be affected by environmental inconsistencies.

Figure 3: Research direction regarding deep learning-driven PHY-layer
technologies.

Secondly, as shown in Fig. 3, we outline the research direction regarding deep
learning-driven PHY-layer technologies. From now until $x$
years212121Depending on the degree of performance deterioration caused by
offline learning and actual wireless channel environment mismatch, $x$ may
differ for each item. If it is based on Fig. 3, channel coding ¡
synchronization ¡ positioning is listed by item with the smallest $x$ value.,
it is expected to have two directions as follows. The first is Multi-Offline
Learning and Adaptation (MOLA), which will perform offline learning on a
number of channel models in advance, file-up to the system, and monitor the
actual radio channel characteristics to apply the appropriate offline learning
to the system. The second is Single Offline Learning and Online Learning
(SOLO), which identifies the performance sensitivity of each radio channel
characteristic factor, and applies offline learning based on the least
sensitive factors to the actual system, and online learning to adapt to the
actual radio channel characteristics online. Next, it is expected that after
$y$ years, it will be used either MOLA or SOLO depending on the radio channel
situation. The classification criteria in this figure are as follows. The MOLA
is expected to take a long time as it will require vast amounts of data bases
and memory, but is expected to be used effectively in some wireless channel
environments. In addition, in radio-channel environments that are not covered
by MOLA, SOLO is expected to be applied in a semi-optimal manner, but this is
a prediction and should not be ruled out that if the SOLO can cover the MOLA
in both performance and implementation terms, it can eventually go in the form
of SOLO.

In this section, we have tried to answer the following research questions:

  * •

How will ML methods impact the physical layer of communication systems?

  * •

Which problems in the physical layer could be solved by ML and what methods
are most suitable?

  * •

Is end-to-end communication system design possible using deep learning?

  * •

How deep learning will be used to optimize physical layer?

  * •

What are the implementation issues of using ML methods is physical layer?

  * •

How does deep learning-based physical layer optimization perform on real data
obtained from test-beds that operate in actual physical environments?

  * •

How does deep learning-based physical layer optimization perform under
imperfect CSI, channel correlation, and other imperfections?

  * •

How can deep learning-based physical layer optimization be combined with the
intelligence in upper layers?

  * •

How to reduce the training workload of deep learning models for physical layer
optimization by using the recent advancement of deep learning such as domain
adaptation and transfer learning?

  * •

How to reduce training data by applying the advancement of generative
adversarial networks to generate artificial data in the physical layer?

##  5 ML at the Medium Access Control Layer

Medium access control (MAC) layer of a cellular networks performs tasks such
as user selection, user pairing for MIMO systems, resource allocation,
modulation and coding scheme selection, power control of uplink transmissions
and random access and handover control. Several heuristic algorithms are in
place currently to address these problems considering the complexity of the
problem. There are no optimal solutions for these problems in real
environments. ML tools must be leveraged to significantly enhance the MAC
scheduler in order to provide significant gains in real environments. While
optimal solutions are not available, significant thought must go on how to
train ML models for these problems. Hence, reinforcement learning frameworks
are most sutiable for problems in which the network can adapt to varying users
conditions such as channel conditions and learn the optimal strategies. For
example, a scheduler must learn to predict the buffer traffic characteristics,
speed and channel variations over time and use these predictions to make
intelligent scheduling decisions. Care must be taken because the state-action
space can grow large very quickly in such a situation. Intelligent deep
reinforcement learning algorithms that can deal with combinatorial actions
spaces and multi-agent environments must be explored for these problems. In
the following, we provide some use cases for which ML can be used in MAC layer
communications.

###  5.1 FL for Orientation and Mobility Prediction in Wireless Virtual
Reality Networks

An elegant and interesting use of FL for solving wireless communication
problem is presented in [29] for minimizing breaks in presence (BIPs) that can
detach the virtual reality (VR) users from their virtual world. The model in
[29] considers a set of base stations (BSs) service a set of wireless VR users
over both uplink and downlink. The uplink is used for tracking information
transmission while the downlink is used for VR image transmission. The VR
users can operate at both mmWave and sub-6 GHz frequencies. The sub-6 GHz band
is used for tracking information transmission while the mmWave band is used VR
image transmission. Different from the existing VR works such as in [30, 31,
32] that assume the VR users are static, the the VR users’ locations and
orientations in [29] will affect the BIPs of each VR user. Since mmWave band
is used for VR image transmission, the blockage effect caused by human body is
considered. Therefore, the purpose of [29] is to minimize the BIP of each VR
user via adjusting user association. To determine user association, the
orientation and mobility of each VR user must be proactively determined.

Federated echo state network (ESN) prediction algorithm is used to proactively
determine the users’ orientations and mobility. The input of the federated ESN
is historical orientations and mobility of each user. The output of the
federated ESN is the future orientations and locations of each user. At each
training iteration, each BS only need to transmit the ESN parameters to other
BSs without transmission of users’ orientation and mobility data. When the
training process is completed, federated ESN algorithm can predict the
locations and orientations of each user. Given the predictions of each VR
users’ locations and orientations, the BSs can optimize user association so as
to minimize the BIP of each user and enhance VR quality-of-experience.

###  5.2 Predictive Resource Allocation in Machine-Type Communications

Most Internet-of-Things (IoT) applications have devices that are stationary or
of low mobility and the traffic originating from such IoT devices has specific
patterns, therefore, ML-based predictive resource allocation is possible by
using the so-called “fast uplink grant” [33]. Such a predictive resource
allocation will decrease the latency of the network and alleviate problems
associated with the random access process for machine-type communications
(MTC) [34]. Some initial results and directions on the predictive resource
allocation for MTC are presented in [35]. However, there are many open
problems to be solved. The first is to study various types of data traffic
originating from MTC and to find the right tools to solve the source traffic
prediction problem. For example, event-driven traffic prediction requires
sophisticated ML tools for event detection and traffic prediction. Second is
optimal predictive resource allocation using online decision-making tools in
various systems such as Non-Orthogonal Multiple Access (NOMA), massive MIMO,
and cell-free massive MIMO. Therefore, it is clear that ML plays a big role in
enabling predictive resource allocation mechanisms in future MTC networks.

###  5.3 Predictive Power Management

Energy Consumption is one of the crucial factors in the design of wireless
networks. Besides the environmental factor, the requirements of IoT devices to
have long battery life is one of the key drivers for continuous exploration of
techniques to conserve energy for future 6G networks as well. Energy
Conservation can be performed at different layers of the system, however, at
the MAC layer, it is considered to be most effective due to direct control of
radio which consumes the maximum power. Therefore, using ML techniques to
predict the traffic and segregate the packet based on priority can improve the
performance of adaptive power saving mechanisms.

Moreover, current wireless networks employ transmit power control or
retransmissions to improve the system performance in high interference
scenarios. Such an approach has a detrimental impact on system performance in
terms of energy efficiency. Predicting the transmit power based on actual
network conditions could result in improved energy and spectrum efficiency of
the overall system. Naturally, reinforcement learning techniques are most
suitable for power control problems.

By deploying ML based algorithms to study the cell level traces collected in a
real network, it is possible to devise models based on predicted traffic
patterns and contextual data for different cells and the corresponding load
metrics. These models can learn the behavior of neighboring interfering users
and adjust the sleeping scheduling and transmit power accordingly. Moreover,
such models can be used to dynamically switch on and off BSs to conserve
energy at higher levels as well.

###  5.4 Asymmetric Traffic Accommodation

Wireless data traffic is asymmetric in nature. Due to which current wireless
networks employ duplexing techniques like time division duplexing (TDD) or
frequency division duplexing (FDD). In TDD systems, addressing the issue of
asymmetric traffic is simple and can be managed based on the traffic load in
downlink and uplink. However, in FDD systems, the frequency bands of downlink
and uplink are separated by frequency gap to provide isolation from self-
interference. Although much progress has been made to provide efficient
cancellation of such interference and enable a true full-duplex system,
however, it is still not mature enough. In FDD systems, the symmetric
allocation of resources between uplink and downlink results in under-
utilization of resources. ML techniques can help to provide intelligent
solutions to such problems by seamless integration of data traces collected
from cells to enable proactive MAC functions rather than traditional reactive
ones.

Recently, the concept of flexible duplex is introduced which allows to
dynamically allocate the resources in both time and frequency domain
simultaneously rather than static TDD and FDD to address asymmetric traffic.
Flexible duplexing allows matching the resource based on traffic pattern even
in single paired FDD. On the other hand, the use of TDD will allow the
allocation of resources to symbol level granularity rather than carrier level
in FDD. Deploying flexible duplexing for broadband communication might be
possible where downlink traffic is more and uplink resources will be used for
downlink transmission. In this case, downlink traffic will have interference
from neighboring cell uplink users with low transmit power and therefore,
corresponding downlink power can be adjusted. ML based algorithms can drive
such techniques in a proactive manner as the entire concept is based on
traffic pattern and network activity data and would result in increased system
performance.

In this section, we have tried to answer the following research questions:

  * •

What is the role of predictive models in MAC layer?

  * •

How ML will help in resource allocation in wireless networks?

  * •

How asymmetric traffic prediction could benefit from ML?

  * •

What ML methods could be used for MTC networks?

  * •

How FL will be used to address mobility for virutal reality?

##  6 ML for Security of Wireless Networks

This section give a brief discussion on the role of ML in security of the
future wireless systems. First, a general road-map towards 6G security is
provided which is then followed by security aspects in wireless medium.

###  6.1 The Road Towards 6G Security

The integration of massive IoT, and the provision of new services, for example
for smart homes, hospitals, transport, and electric grid systems in 5G will
exacerbate the security challenges. Among the prominent solutions to govern
the network security, ML-based solutions have grasped the most attention due
to the exacerbating amount of traffic expected in 5G. In 6G, the speeds will
grow up by many-folds, latency will be non-observable, connectivity is poised
to be ubiquitous, and critical infrastructures will be automated using the
underlying network infrastructure. Therefore, the network security paradigm
will be further shifted to extremely agile, dynamic and autonomous systems.
ML-based security solutions, thus, will be inevitable.

With the conglomeration of diverse IoT devices and services, UAVs, V2X, and
smart home appliances within 6G networks, differentiating between a security
attack and legitimate traffic will be practically impossible or unmanageable
without using ML tools [36]. Analysis of humongous amount of data for
monitoring the network traffic-in-transit for security would require designing
proactive, self-aware and self-adaptive ML techniques. Since critical
infrastructures such as electricity smart grids, transportation and health-
care systems will be connected, proactive security measures that require
continuous intelligence gathering, and using that intelligence to mitigate the
possibility of security risks and lapses, will necessitate the needed storage
and computing resources in zero latency vicinity. Such systems would, thus,
require using ML to proactively transport ML-based security systems to
different network perimeters, as well as scaling the required resources
dynamically without any delay. Hence, ML will be a stepping stone to predict
and provide the needed security systems in those perimeters on one hand, and
the extend the necessary resources through scaling up the resources from the
pool of available virtual resources on the other hand.

ML-based security approaches need to be considered from end-to-end network
perspectives in 6G. Currently, ML has been used in different services, parts
of the networks, and different networked nodes. In 6G, the use of ML must bye
synchronized across the network. For example, consider the case of intelligent
ML-based spectrum sharing. Intelligent spectrum sharing requires the spectrum
information to be securely shared among peers competing for the same frequency
slot. The first case would be to secure the sharing of information among the
contending and provider peers. ML can be used to recognize the legitimacy of
the contending peers, and in securely sharing information. However, adjusting
the upper layers after hopping to the available spectrum as agreed by the
providing and contending peers, will also need security to be in place. For
example, adjusting secure routing procedures in the network layer after
decision taken in the physical layer. Such systems employing ML in each layer
e.g. in the physical layer regarding secure spectrum sharing and in the
network layer regarding secure route establishment and security of the payload
afterwards, require synchronized ML procedures.

###  6.2 Wireless Security

The inherent openness of wireless medium makes it susceptible to interference.
Interference can be either intentional or un-intentional. Un-intentional
interference could be caused by devices close to us that may transmit at
higher power levels as instructed by their controllers. Intentional
interference, on the other hand, corresponds to adversarial attacks on a
system. Adversarial attacks are detrimental for a system as they may hamper
the communication among various nodes and may potentially stop important
communication. There are two aspects for wireless security that must be
studied - defense and attack. Defensive mechanisms include cryptography and
the likes, while attacks refer to mechanisms where proactively an attack such
as jamming or eavesdropping is performed to secure the future transmissions.
Such security-related studies not only allow for the analysis of the system
vulnerabilities but also enable to undermine an enemy system capabilities.

The fast pace of research in the field of ML can potentially enable every
device to possess some sort of intelligence that can be used either in a
positive or in a negative manner. If such capabilities exist with the
malicious devices i.e., the ones that want to intentionally cause
interference, then it is a threat to the security of the various devices that
co-exist in the same environment. It is thus highly important that devices be
intelligent to know everything about the adversary so as to limit the
effectiveness of attacks.

Typically, such problems have been addressed via game theory or optimization
frameworks. While they give good insights, they often assume static
environments, or static action space for an adversary etc which may not be the
case when an adversary himself possesses the ML capabilities. Therefore, we
must study these systems both from attack [37] and defense [38] perspective.
From an attack perspective, we need to design ML models that can learn the
environment in real-time and stop the adversary from communicating or
interfering with the required network. From a defense perspective, we need to
design a communication system that is robust against any kind of attacks and
adversarial ML mechanisms can be used for the same to design robust
techniques.

In this section, we have tried to answer the following research questions:

  * •

What is the role of Machine Learning in 6G Security (beyond ML-based security
in 5G)?

  * •

What aspects of security, physical layer, mac layer, network layer can be
addressed via Machine Learning?

  * •

What and where does machine learning in security find use cases? Defense
applications as an example?

##  7 ML at the Application Layer

ML solutions directly embedded on the wireless communication nodes at the
lower layers, with advanced features such as context awareness, performance
optimization, and multi-agent reinforcement learning, will enable more
reliable and stable per user and per-application data rate, peak data rate,
air-interface latency, spectrum efficiency, energy efficiency. At the same
time Embedded ML solutions on the wireless communication nodes at the
transport layer or the application layer, with sensor fusion techniques and
with the capacity to run ML as a service, will improve experience sharing,
remote control capacity, seamless connectivity, and services.

Context-aware systems, in particular, provide the capacity to implement
services that maximize the application’s safety while minimizing the
application’s explicit interaction with the environment. In general, a set of
rules has to be specified for the possible contextual configurations and each
rule is assigned to a specific service in the context-aware system. This is a
common problem to determine and limit the set of possible context
configurations. Instead of a rule-based approach, ML can be used to predict
all possible and meaningful context configurations. This ML approach can use
the previous choice about the service and can adapt itself by a new choice
about the service from user/application feedback information. A variety of ML
techniques can help to develop general-purpose context-aware applications
without needing to define a priori rules and elements of the context. This
context-aware application can provide service proactively by using different
types of learning algorithms in an operational environment that can be smart
and continuously changing. The user preferences (choice for services) may also
change over time so that an adaptive learning algorithm would certainly be
preferable. The middleware layer plays a vital role in the context-aware
system. The middleware is responsible for context modeling, context reasoning
and controlling sensors and data sources, appliances and devices based on the
decision from the context-aware application layer.

Making ML available as a service on wireless communication nodes will
flexibility and power to the communication networks. Four key trends are
making ML more accessible to users and companies: (1) improved processing
power, (2) reduced costs for data storage and processing, (3) expanding data
availability, and (4) improved techniques, like the emergence of cloud-based
deep learning solutions. Hybrid cloud and fog computing might likely further
extend such accessibility by making ML available as a service for users and
applications in the application layer of wireless communication nodes.

###  7.1 ML for 6G Network Performance Management Automation

5G advanced/6G Mobile networks have increased complexity which demands smarter
network features and approaches to handle any Key Performance Indicator (KPI)
degradation, anomaly detection, and trend prediction to keep KPI within the
required thresholds [39]. This can be achieved by applying ML and software
defined networking (SDN) solutions. ML will enhance the decision-making
process to keep excellent KPI network service levels. For 6G, a new approach
is required for the management and implementation of Radio Access Networks
(RAN). Example ideas include adding ML to baseband processes, using a
virtualized container-based RAN compute architecture, and by running the
containers close to the Mobile Edge Computing (MEC) servers to achieve latency
as low as 1 ms. 6G virtualization for RAN and CORE both are moving to
container-based applications from open stack VM based due to efficiency and
security. ML enables anomaly detection in KPI trend spikes, success and
failure rates, handover KPIs, accessibility KPIs, availability KPI as well as
integrity, privacy, and security KPIs.

Enabling ML modeling for accessibility, availability, mobility, and traffic
performance using the 6G network real-time data extracted from UE measurement
reports will enhance and automate network performance management to keep KPI’s
within predefined thresholds. ML enables the management automation of 6G
dynamic mobile networks with smart adaptive cells. This could enhance the
performance of coverage, throughput, QoS prediction, automatic network
configuration, power control, operation, maintenance, fault management, power-
saving, and beam management. Fig. 4 shows ML enhancing 6G network performance
management aspects.

Figure 4: ML enhancing 6G network performance management.

###  7.2 ML-Aided UAV Control

One of the major ultra-reliable low-latency communications (URLLC)
applications is to control unmanned aerial vehicles (UAVs) over wireless links
in new mission-critical scenarios such as providing emergency wireless
networks for the disastrous area and delivering first aid kits in rescue
missions [40]. Therein, the control of UAVs requires stability guarantee,
which is only possible if the wireless communications can assure case-specific
targets of ultra-reliability and low-latency. In 5G URLLC, transmission
techniques such as short packet transmission and spatial/frequency/temporal
diversity, are considered to achieve the $99.999\%$ reliability and $1$ms
latency targets. However, considering control techniques to guarantee
(physical) stability allows relaxing the latency and reliability requirements
for transmission in mission-critical applications [41, 42]. Additionally, due
to various (communication and/or control) constraints, the communication and
control co-design (CoCoCo) in real-time UAV use cases as well as many other
automation applications can become a very complex problem. To overcome the
complex nature of CoCoCo problems, the regression/adaptation/learning
capabilities in ML methods can be utilized. In the following, two use cases of
CoCoCo are described briefly.

As the first exemplary use case, a single UAV is controlled by a ground
controller to reach a target destination. At each control cycle, the UAV state
(velocity and distance to the destination at each time instant) is downloaded
to the controller, and the optimal action (acceleration) computed by a ANN in
the controller is uploaded (UL) to the UAV within a time deadline. The UL
transmission power can be tuned based on the download latency, to meet the
time deadline. When the environment dynamics is learned and the transmission
cost becomes high, the UAV switches to autonomous mode by receiving the ANN
model from the ground controller [43]. As a result, the UAV is always
controlled by a well-trained ANN even to complete the desired mission.

As another example use case consider a swam of autonomous UAVs is dispatched
from a source to a target destination. Each autonomous UAV is controlled by
running a pair of ANN to obtain mean-field (MF) approximation of other UAVs
states (MF neural network) and then to compute its optimal action (action
ANN). To reduce the risk of collision, the action ANN is affected when the
relative distance of UAVs becomes small or their relative speed becomes large.
The stability of this control method is guaranteed when the initial states of
the UAVs are exchanged. Moreover, this ANN-based control method can reduce
transmission power[41, 44].

In both examples, ML and communication are considered together and as a
result, the reliability, safety, and energy consumption of the UAV control are
improved. This way of control where both the ML training and the
communications benefit from each other is extensively studied in [45]. Other
possible ML and communication co-design use-cases such as [46] intelligently
utilize communications resources with the help of predictions provided by the
ML, and [47] solve a distributed ML problem in a communication efficient way.
Based on these research examples, considering communication and ML/control can
provide many advantages. However, the control and communications co-design is
still a challenging issue that needs to be addressed further in 6G.

###  7.3 Opportunistic Data Transfer in Vehicular Networks

Parallel to the technological advancements that drive the development of 6G
networks, road vehicles are subject to a step-wise evolution process that aims
to improve traffic safety and efficiency by introducing means of connectivity
and automation. As a side-effect of this development, the manifold sensing
capabilities of modern cars will allow exploiting vehicles as moving sensor
nodes that can cover large areas and provide highly-accurate measurements.
Crowdsensing-enabled services such as the distributed generation of high-
definition environment maps will then be available to improve the situation
awareness of the vehicles themselves.

Data transfer in vehicular networks is a challenging task since the channel
dynamics depend on a large amount of external and environment-specific impact
factors. Vehicular communications systems have to be compliant with very high
velocities on highways and be able to cope with sporadic line-of-sight
situations in inner cities. As a result, moving vehicles frequently encounter
low-connectivity regions where link loss, packet errors are highly probable
which results in a need for retransmissions.

Client-based context-aware network optimization techniques such as
opportunistic data transfer and multi-connectivity offer the potential of
achieving relief without requiring to extend the actual network
infrastructure. Hereby, ML-based data rate prediction allows selecting network
interfaces and schedule data transmissions based on the anticipated resource
efficiency within a window of delay tolerance related to application-specific
requirements for the age of information of the sensor measurements. This
approach allows us to proactively detect and avoid highly resource-consuming
transmissions.

Although first feasibility studies [48] that make use of passive downlink
indicators practically demonstrated the achievable benefits of this approach,
purely client-based techniques are almost unaware of the network load and the
potentially available resources within the connected cell which ultimately
limits the achievable data rate prediction accuracy.

Within 6G networks, these limitations could be overcome through a cooperative
approach where the network infrastructure actively shares its load information
with the clients via control channels.

Figure 5: Opportunistic data transfer in vehicular networks.

###  7.4 Software Development Aspects

For real-world usage, choosing a ML model for solving a specific problem
cannot be solely decided based upon prediction performance metrics.
Limitations on computation, energy resources, and requirements in response
time have an impact on software technologies used to extract data, store it,
train ML models, and make predictions. However, relying on ML for networking
and wireless communications will also have a profound impact on software
development practices needed for providing results while ensuring quality.

Research in engineering of ML solutions in wireless communication must address
also challenges in development practices. If the trends in wireless systems
software development will shift increasingly towards ML-based methods, the
main challenge will be related to the engineering paradigm change from
deterministic, classic requirements-driven projects and processes towards a
data-driven monitor, extract data, learn, predict cycles in development of the
systems and services. Consequently, one of the first steps is that existing
engineering tools, methods and processes should evaluated based on their
adaptivity to above described ML-driven development loop. This gives an
overacrching understanding of the magnitude of changes and investments that
are required in industry domains.

In parallel, with data science and ML gaining in popularity, software problems
specific to the field also became apparent. Systems relying heavily on ML not
only share the same issues that other software systems encounter, but have
additional long-term shortcomings that can incur high maintenance costs for
real-world usage. In the past years, DataOps, a movement inspired by DevOps,
has emerged to better deal with those problems specific to data science and ML
[49]. This movement aims at providing development processes and software
technologies to improve quality when delivering data-based solutions in a
fast-changing world. To provide ML solutions at a large scale for wireless
systems, 6G will have to embrace development practices from Agile software
development, DevOps, and DataOps. Moreover, movements like DevOps and DataOps
are relatively new and in an ever-evolving state. Thus, because networking and
wireless communications have their specificities and requirements of their
own, people with an active role in the development of 6G might also have to
take an active role in these movements.

In this section, we have tried to answer the following research questions:

  * •

What is the vision for 6G Network Management?

  * •

How will ML enable, enhance and automate the network performance management
for 6G Mobile networks?

  * •

How will ML will enable, enhance and automate the 6G mobile network
optimization?

  * •

What existing software development practices, processes, and technologies will
be needed in order to incorporate ML in large scale real-world networking and
wireless communication technologies?

  * •

What are specificities of 6G that will require to adapt existing or create new
Agile, DevOps, or DataOps practices, processes, and technologies?

##  8 Standardization Activities

Various standardization bodies like 3GPP, and International Telecommunication
Union (ITU), but also the 5GAA (5G Automotive Association) have started
evaluating ML in 5G and future networks. From a standardization perspective,
the ML models and algorithms will not be standardized [50]. Other bodies such
as the ORAN alliance have started defining open interfaces in order to
exchange relevant information between various parts of the protocol stack.
Specifically, they have defined entities’ names as a real-time intelligent
controller and a non-real-time intelligent controller. Non-real time RIC is
one where the training for the ML models happens using the data captured by
lower layers. This learning happens very slowly and hence the word non-
realtime.

This learned model is fed into the real-time RIC which uses this model on
real-time data and makes real-time decisions in an online fashion. Such
systems can be deployed in core networks or in RAN based on the type of data
that can be collected.

The discussion of introducing ML capabilities in the 3GPP RAN is still in the
preliminary stage in the standardization. The autonomous network is an
important topic for RAN considering the complexity of future networks. Six
levels of automation are proposed for the RAN. Level zero (L0) starts with a
manual operating network and ends with L5 at fully autonomous networks with no
human involvement at any stage. The levels are summarized in the Table 1 along
with the tasks [51].

Level | Task categories
---|---
Execution | Awareness | Analysis | Decision | Intent translation |
L0 | Manual operating network | Human | Human | Human | Human | Human
L1 | Assisted operating network | Human & Network system | Human & Network system | Human | Human | Human
L2 | Preliminary autonomous network | Network system | Human & Network system | Human & Network system | Human | Human
L3 | Intermediate autonomous network | Network system | Network system | Human & Network system | Human & Network system | Human
L4 | Advanced autonomous network | Network system | Network system | Network system | Network system | Human & Network system
L5 | Fully autonomous network | Network system | Network system | Network system | Network system | Network system

Table 1: Network Automation Level

Additionally, it is also required to define

  * •

signaling support for ML training and execution,

  * •

data required by the ML algorithms either reported by the user equipment (UE)
or collected from an NG-RAN node, and

  * •

outputs generated by the algorithms to be delivered to the network including
the network functions and core network.

Also, if the UE has the capability to support at least a part of ML inference
on board then it becomes relevant to study how the ML-enabled UE obtains an
updated ML model and intermediate output based on dynamic environment changes
and application. It is unfeasible to pre-load all possible models on-board
because of limited storage space in the UE’s. Therefore, the ML model
downloading or transfer learning is needed. ITU-T Rec. Y.3172 defines a
technology-agnostic logical architecture model for the high-level machine
learning requirements – such as interfaces, support for heterogeneous data
sources, machine learning mechanisms – in future networks. The actual underlay
network technology (e.g., 4G, 5G, 6G, IEEE 802.11) is virtually mirrored by a
digital twin – referred to as _closed-loop subsystem_ – which is utilized to
safely explore the outcomes of different machine learning-enabled acting
options.

## Acknowledgement

This draft white paper has been written by an international expert group, led
by the Finnish 6G Flagship program (6gflagship.com) at the University of Oulu,
within a series of twelve 6G white papers to be published in their final
format in June 2020.

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