# Generative AI for Secure Physical Layer Communications: A Survey

Changyuan Zhao, Hongyang Du, Dusit Niyato, Fellow, IEEE, Jiawen Kang, Zehui
Xiong, Dong In Kim, Fellow, IEEE, Xuemin (Sherman) Shen, Fellow, IEEE, and
Khaled B. Letaief, Fellow, IEEE C. Zhao, H. Du, and D. Niyato are with the
School of Computer Science and Engineering, Nanyang Technological University,
Singapore (e-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>dniyato@ntu.edu.sg).J. Kang is with the School of Automation, Guangdong
University of Technology, China. (e-mail: kavinkang@gdut.edu.cn). Z. Xiong is
with the Pillar of Information Systems Technology and Design, Singapore
University of Technology and Design, Singapore (e-mail: zehui
xiong@sutd.edu.sg).D. I. Kim is with the Department of Electrical and Computer
Engineering, Sungkyunkwan University, Suwon 16419, South Korea
(email:dikim@skku.ac.kr).X. Shen is with the Department of Electrical and
Computer Engineering, University of Waterloo, Canada (e-mail:
sshen@uwaterloo.ca). Khaled B. Letaief is with the Department of Electrical
and Computer Engineering, Hong Kong University of Science and Technology, Hong
Kong (e-mail: eekhaled@ust.hk).

###### Abstract

Generative Artificial Intelligence (GAI) stands at the forefront of AI
innovation, demonstrating rapid advancement and unparalleled proficiency in
generating diverse content. Beyond content creation, GAI has significant
analytical abilities to learn complex data distribution, offering numerous
opportunities to resolve security issues. In the realm of security from
physical layer perspectives, traditional AI approaches frequently struggle,
primarily due to their limited capacity to dynamically adjust to the evolving
physical attributes of transmission channels and the complexity of
contemporary cyber threats. This adaptability and analytical depth are
precisely where GAI excels. Therefore, in this paper, we offer an extensive
survey on the various applications of GAI in enhancing security within the
physical layer of communication networks. We first emphasize the importance of
advanced GAI models in this area, including Generative Adversarial Networks
(GANs), Autoencoders (AEs), Variational Autoencoders (VAEs), and Diffusion
Models (DMs). We delve into the roles of GAI in addressing challenges of
physical layer security, focusing on communication confidentiality,
authentication, availability, resilience, and integrity. Furthermore, we also
present future research directions focusing model improvements, multi-scenario
deployment, resource-efficient optimization, and secure semantic
communication, highlighting the multifaceted potential of GAI to address
emerging challenges in secure physical layer communications and sensing.

###### Index Terms:

Generative AI, physical layer communications, physical layer security,
wireless sensor network, anomaly detection.

##  I Introduction

Generative Artificial Intelligence (GAI) represents a transformative category
of Artificial Intelligence (AI) technologies capable of creating content,
ranging from text, images, music, to complex simulations [1]. As a kind of
unsupervised learning, GAI is trained on vast amounts of data to understand
the underlying structure and dynamics of that data. Unlike traditional AI,
which primarily focuses on analyzing and interpreting data, GAI takes a step
further by generating new, original outputs based on learned patterns and
datasets [2]. Once trained, these models can produce outputs that mimic the
original data’s style, tone, and complexity, often indistinguishable from
content produced by humans [3]. Reflecting on its inherent capabilities, GAI
has been successfully deployed in a wide range of mature applications across
different fields, including Stable Diffusion [4], DALL-E 3 [5], and ChatGPT
[6], etc. Beyond its prowess in generating varied content forms, GAI also
demonstrates powerful capabilities in enhancing cybersecurity measures, by
generating sophisticated simulations and datasets for threat detection and
system strengthening [7]. The innovative essence and wide-ranging
applicability of GAI have captivated the research community, leading to an
upsurge in interest to uncover its capabilities in addressing intricate
challenges and driving innovation across various fields.

In wireless communications, security is a critical aspect of modern
information technology, ensuring the confidentiality, integrity, and
availability of data transmitted across networks [8]. Techniques such as
encryption, secure socket layers, and digital signatures are employed to
protect sensitive information during its transmission over the internet or
other communication networks [9]. In the Open Systems Interconnection model of
communications [10], physical layer security plays a pivotal role in
protecting communication networks by utilizing the inherent physical
characteristics of the communication channel to thwart unauthorized access and
guarantee data integrity [11]. This fundamental security layer capitalizes on
the inherent unpredictability of channel properties, serving to enhance
conventional encryption techniques by adding an extra layer of defense against
eavesdropping and cyber-attacks. Given its critical significance, researchers
have dedicated extensive efforts to conduct in-depth studies on physical layer
security [12].

With the advancement of AI, the integration of Deep Learning (DL) methods has
revolutionized communication security, offering enhanced capabilities for
anomaly detection, automatic threat identification, and adaptive security
measures based on real-time data analysis [13]. For instance, Convolutional
Neural Networks (CNNs) are employed to design physical layer security
techniques such as in the development of an intrusion detection system [14],
multi-user authentication [15]. In addition, Recurrent Neural Networks (RNNs)
have found utility in various studies including automatic modulation
classification [16], secure channel coding [17], and intrusion detection [18].

However, traditional AI methods often fall short in addressing physical layer
security challenges due to their inability to dynamically adapt to the
continuously changing physical characteristics of transmission channels and
the sophisticated nature of modern cyber threats [19]. Specifically,
traditional AI models are typically trained on datasets from specific
environments, limiting their effectiveness when deployed in unfamiliar
conditions. Furthermore, the complexity and variability of noise patterns,
signal interference, and channel conditions within the physical layer lead to
difficulties in collecting sufficient labeled data for physical layer attacks.
This challenge necessitates the development of sophisticated AI models capable
of learning from and adapting to these ever-changing environmental factors,
thereby ensuring the continuous maintenance of robust security measures [20].

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AI for Secure Communication [23], [24], etc. , minimum width=7.0cm, text
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in Physical Layer, minimum width=4.0cm [Confidentiality, Authentication,
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and Authentication, minimum width=4.0cm, base=midpoint, [Secure Communication,
minimum width=4.0cm, [Encryption: GAN [28], SVAE [29], etc.
Anti-eavesdropping: VQ-VAE [30], WGAN-GP [31], etc., minimum width=7.0cm,
base=midpoint, text height=0.53cm]][Communication Authentication, minimum
width=4.0cm, [ RF Fingerprinting: GAN [32], Triple-GAN [33], etc.
CSI: CGAN [34], etc. CIR: VAE-PLA [35], etc. , minimum width=7.0cm ,
base=midpoint, text height=0.53cm]] ] [Communication Availability
and Resilience, minimum width=4.0cm, base=midpoint, [Anti-jamming Strategy,
minimum width=4.0cm, [ Jamming Recognition: GAN [36], AC-VAEGAN [37], etc.
Anti-jamming: GAN [38], ADRLDN [39], etc. , minimum width=7.0cm,
base=midpoint, text height=0.53cm]][Spoofing Defense, minimum width=4.0cm,
[GAN [40], CBEGAN [41], SJG-GAN [42], etc., minimum width=7.0cm]] ]
[Communication Integrity, minimum width=4.0cm, [Anomaly Detection, minimum
width=4.0cm, [ Score-based: SAIFE [43] , $\beta$-VAE [44], etc.
Prediction-based: E-GAN [45], CGAN [46], etc. , minimum width=7.0cm,
base=midpoint, text height=0.53cm] ] [Data Reconstruction, minimum
width=4.0cm, [SARGAN [47], VAE-GAN [48], CDDM [49], etc., minimum
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[Conclusion, minimum width=4.0cm, ] ]

Figure 1: The structure of the survey paper, where we introduce GAI methods
for physical layer security through Communication Confidentiality and
Authentication (Section III), Communication Availability and Resilience
(Section IV), and Communication Integrity (Section V).

Confronted with the critical challenges in secure physical layer
communications, and recognizing the distinct advantages provided by GAI, this
paper provides a thorough survey of GAI’s applications in tackling various
issues in physical layer security.

###  I-A Related Surveys and Contribution

TABLE I: SUMMARY OF RELATED SURVEYS Scope |  Reference |  Emphasis | Overview
---|---|---|---
GAI for Communication Networks |  [21] |  GAI in mobile networks |  A survey of the recent work in the field of GAI with application to mobile telecommunications networks
|  [50] |  Edge-Cloud GAI |  An overview of research activities related to AIGC, GAI, and mobile edge intelligence
|  [51] |  GAI-driven SemCom |  A summary on the interplay between GAI and SemCom in wireless communication networks
|  [22] |  GAI for Physical Layer Communications |  A survey of GAI’s applications to address diverse problems in physical layer communications
AI for Secure Communication |  [23] |  AI for IoT security |  A summary of the contribution of AI to the IoT security in Edge computing
|  [52] |  AI for Security and Privacy of 6G |  A overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers
|  [53] |  AI-based Intrusion Detection System |  A survey on machine learning-based intrusion detection systems for secure communication in IoTs
|  [54] |  AI in IoT Security |  A overview of applying machine learning for cyber threat detection in IoT environments
|  [55] |  AI-based Physical Layer Security |  A summary on intelligent wireless physical layer security by concentration on physical layer authentication, antenna selection, and relay node selection
|  [56] |  AI-assisted Secure Data Transmission |  An in-depth analysis of the role of AI in optimizing and designing the intelligent physical layer security techniques
|  [24] |  AI-based Physical Layer Security |  A survey about employing DL-based physical layer security techniques for solving various security concerns in 5G and beyond networks

####  I-A1 GAI for Communication Networks

Recent literature has witnessed a notable increase in the exploration of GAI
applications within communication networks (Table I). The work [21] delves
into the utilization of GAI to address contemporary challenges in mobile
telecommunications networks. This article underscores the pivotal role of
generative AI in the advancement of mobile network technologies, particularly
in the overcoming of existing obstacles. [50] shifts the focus to the
deployment of Artificial Intelligence Generated Content (AIGC) in mobile
networks, providing comprehensive insights into Generative AI and mobile edge
intelligence. Additionally, [51] investigates the interplay between GAI and
Semantic Communication (SemCom) in wireless networks. Their research
demonstrates the utility of GAI in the creation, transmission, and efficient
management of information within these networks. Moreover, the authors in [22]
present an analysis of GAI applications in the physical layer, addressing
various applications but the security issues are not the main focus.

####  I-A2 AI for Secure Communication

AI has significantly transformed the landscape of communication network
security and privacy. In [52], a systematic overview is presented on
prospective technologies for 6G networks, focusing on the physical,
connection, and service layers, along with lessons learned from existing
security architectures. The authors in [23] discuss the contribution of AI to
Internet of Things (IoTs) security within Edge Computing (EC) environments,
particularly emphasizing AI’s role in augmenting security features. Regarding
attack detection, [53] provides a detailed survey on AI-based intrusion
detection systems, with a focus on securing communication within the IoT.
Similarly, [54] delves into machine learning applications, with a specific
emphasis on cyber threat detection in IoT environments. For Physical-Layer
Security, the utilization of AI in optimizing and designing intelligent
physical layer security techniques is thoroughly explored in [56]. [55]
introduces intelligent wireless physical layer security by concentrating on
physical layer authentication, antenna selection, and relay node selection. In
a related vein, [24] investigates DL based physical layer security techniques,
concentrating on their application in addressing various security concerns in
5G and beyond networks. However, it is noted that there is a lack of detailed
analysis regarding the role of GAI in physical layer security.

Distinct from existing surveys and tutorials, our survey distinguishes itself
by specifically focusing on the integration of GAI in secure physical layer
for communication networks. Unlike previous works, which either broadly
address GAI applications in communication networks or delve into AI’s role in
network security without a concentrated emphasis on GAI, this survey offers a
unique perspective by marrying the capabilities of GAI with the requirements
of physical layer security. It fills a critical gap in the literature by
providing an in-depth analysis of how GAI can enhance security measures,
detect and mitigate threats in the physical layer that have been previously
underexplored or only briefly mentioned.

The key contributions of this paper are summarized as follows:

  * •

Our comprehensive analysis reveals how to employ GAI models to enhance key
security properties such as communication confidentiality, authentication,
availability, resilience, and integrity. These advancements are facilitated by
GAI’s ability to understand complex data distributions, perform encrypted data
transformation and processing, and detect cyber threats and anomalies within
the network infrastructure. This summary provides essential insights for
further exploration and development of GAI applications in physical layer
security.

  * •

We explore how GAI addresses the challenges of data sparsity and
incompleteness in physical layer security, which significantly impact the
efficacy of traditional AI models. GAI’s contribution to data reconstruction
and augmentation showcases its unparalleled ability to enhance physical layer
security, surpassing the limitations of tranditional AI approaches.

  * •

We outline crucial future research directions for the applications of GAI in
physical layer security, including model improvements, multi-scenario
deployment, resource-efficient optimization and secure semantic communication.
These directions are considered from multiple perspectives, underscoring the
multifaceted potential of GAI to address emerging challenges.

The structure of this survey is outlined in Fig. 1. Section II introduces the
fundamental concepts of GAI and offer a review of related works.  In section
III, a comprehensive exploration into Communication Confidentiality and
Authentication is presented. Section IV discusses approaches for Communication
Availability and Resilience. Section V introduces GAI methods for
Communication Integrity. Section VI discusses future research directions, and
Section VII concludes the paper. Additionally, Table II lists the
abbreviations commonly employed throughout this survey.

TABLE II: LIST OF ABBREVIATIONS Abbreviation |  Description |  Abbreviation |  Description
---|---|---|---
AI |  Artificial Intelligence |  GAI |  Generative Artificial Intelligence
CNN |  Convolutional Neural Network |  RNN |  Recurrent Neural Networks
AIGC |  Artificial Intelligence Generated Content |  DL |  Deep Learning
DAI |  Discriminative AI |  AE |  Autoencoder
VAE |  Variational Autoencoder |  GAN |  Generative Adversarial Network
DM |  Diffusion Models |  DRL |  Deep Reinforcement Learning
WGAN-GP |  Wasserstein GAN with Gradient Penalty |  CGAN |  Conditional GAN
ACGAN |  Auxiliary Classifier GAN |  AAE |  Adversarial Autoencoder
SNR |  Signal-to-Noise Ratio |  JNR |  Jamming-to-Noise Ratio
PCA |  Principal Component Analysis |  MIMO |  Multi-Input Multi-Output
CIR |  Channel Impulse Response |  CSI |  Channel State Information
RF |  Radio Frequency |  LSTM |  Long Short-Term Memory
SU |  Secondary User |  PU |  Primary User
SemCom |  Semantic Communication |  IoT |  Internet of Things
EC |  Edge Computing |  EH |  Energy Harvesting
JSCC |  Joint Source Channel Coding |  BER |  Bit Error Rate
AWGN |  Additive White Gaussian Noise |  BLER |  Block Error Rate

##  II Background Knowledge

In this section, we delve into the security challenges inherent to the
physical layer of communication networks, arguing that addressing security at
this foundational level is of paramount importance. Furthermore, we introduce
the fundamental concepts of GAI, including its architecture, classification,
and basic models.

###  II-A Security Issues in Physical Layer

TABLE III: THE USE OF GAI IN THE PHYSICAL LAYER AND ITS POTENTIAL SUPPORT FOR SECURITY Issues Model |  GANs |  AEs and VAEs |  DMs |  Communication & Sensing Perspectives
---|---|---|---|---
|  |  |
Confidentiality |  ⚫ key generation ⚫ channel response approximations ⚫ anti-eavesdropping communications |  ⚫ wiretap code design ⚫ transceiver design ⚫ VAE-based JSCC |  - |  Potential benefits for communication: ✓ Robustness to the changing environment ✓ Simulate noise channel effects ✓ Utilize time-varying information ✓ Extract valuable features various data
Availability |  ⚫ jamming recognition ⚫ anti-jamming strategy |  - |  -
Resilience |  ⚫ spoofing recognition ⚫ spoofing defense |  - |  -
Integrity |  ⚫ sensors anomaly detection ⚫ signals anomaly detection ⚫ radio anomaly detection ⚫ spectral information completion ⚫ electromagnetic data reconstruction |  ⚫ spectrum anomaly detection ⚫ sensors anomaly detection ⚫ DSSS signals reconstruction |  ⚫ noise elimination |  Potential benefits for sensing: ✓ Identify abnormal sensors ✓ Not affected by data imblance ✓ Avoid complex parametric analysis of the signals ✓ Not require any information of the missing band locations
Authentication |  ⚫ RF fingerprinting authentication ⚫ CSI authentication |  ⚫ CIR authentication |  -

Security at the physical layer is deemed paramount compared to other layers
since it provides the foundation for all subsequent security protocols [57].
Therefore, a breach at this foundational level will jeopardize the entire
communication system. This layer is susceptible to a broad spectrum of
physical threats, including eavesdropping, jamming, and spoofing, making it a
critical point of vulnerability that must be robustly protected [58]. By
securing the physical layer, potential attacks can be preemptively thwarted,
thereby preventing attackers’ initial access points for further intrusions.

The subsequent discussion will introduce the CIA triad [59]: Confidentiality,
Integrity, and Availability, alongside two additional critical focuses:
Resilience and Authentication in physical layer security.

  * •

Communication Confidentiality: Communication confidentiality in the physical
layer involves the use of techniques and mechanisms to secure data
transmission over communication channels, preventing unauthorized access and
eavesdropping. This approach leverages the properties of the physical layer,
such as noise and signal characteristics, to enhance security by making it
difficult for attackers to intercept or decode the transmitted information
[60].

  * •

Communication Authentication: Communication authentication at the physical
layer is a critical security measure that verifies the identities of entities
engaged in data exchange to thwart impersonation and unauthorized access. This
verification leverages unique attributes intrinsic to the transmission’s
physical medium, such as radio-frequency fingerprints or specific channel
properties. By authenticating that the communication is genuine and emanates
from a verified source, this process significantly bolsters security and
integrity in the exchange of data [61].

  * •

Communication Availability: Ensuring communication availability at the
physical layer, particularly through anti-jamming measures, involves deploying
strategies and mechanisms to protect wireless communication networks from
deliberate interference or jamming attacks. Techniques such as frequency
hopping and direct sequence spread spectrum are pivotal, as they disperse the
signal across a broader bandwidth, complicating the attacker’s ability to
disrupt communications [12].

  * •

Communication Resilience: Communication resilience at the physical layer,
particularly in safeguarding against a range of attacks, where spoofing
attacks being a typical example, necessitates the implementation of strategies
aimed at detecting and neutralizing attacks signals. Central to this defensive
approach is the use of unique physical features or signatures, such as Radio
Frequency (RF) fingerprints or channel state information. By exploiting these
intrinsic properties, networks are equipped to distinguish authentic signals
from those fabricated by spoofers, significantly maintaining secure and
reliable communications [62].

  * •

Communication Integrity: To safeguard communication integrity at the physical
layer, it is essential to detect anomalous data and complete missing
information [63]. Techniques such as DL algorithms are employed to learn
normal behavior patterns and subsequently identify outliers or irregularities
in real-time data flows. Furthermore, data reconstruction techniques are
applied to correct or mitigate the impact of these anomalies and incomplete
data, guaranteeing the precise and dependable transmission of information
[64].

###  II-B Overview of GAI

GAI aims to learn the underlying features of input data to generate new
content that is similar to real data, in contrast to Discriminative AI (DAI),
which focuses on predicting the probability or labels of data. GAI is capable
of generating a wide variety of data, including text, images, videos, and so
on [1]. Usually, these generated outputs are refereed as AIGC. With the
widespread adoption of AIGC, there has been a significant boost in the
efficiency of content creation, even revolutionizing the production paradigms
of several companies and individual creators.

Currently, GAI models used in commnication networks can be categorized as
follows:

  * •

Autoencoder (AEs) and Variational Autoencoders (VAEs): An Autoencoder is a
type of artificial neural network used to learn efficient codings of unlabeled
data in an unsupervised manner [25]. It works by compressing the input into a
lower-dimensional code and then reconstructing the output from this
representation as close as possible to the original input. By shifting from a
deterministic encoding process to a probabilistic one, VAEs can learn to
represent input data as a distribution in latent space [65]. Through latent
distributions, VAEs generate new instances that resemble the input data by
sampling from the learned distribution in the latent space, making them highly
effective in tasks including image generation, data augmentation, and anomaly
detection [66]. Building upon the foundational principles of VAEs, more
advanced variants such as the Vector Quantized-Variational Autoencoder (VQ-
VAE) have been developed, which incorporates a discrete latent representation
through vector quantization to improve the generation quality [67]. In
summary, AEs and VAEs offer significant benefits, including their ability to
learn complex distributions and generate new data points. However, they have
limitations such as the tendency to produce blurry outputs, and challenges in
balancing the reconstruction fidelity and the latent space regularization
during training [3].

  * •

Generative Adversarial Networks (GANs): Generative Adversarial Network model
is a form of unsupervised learning [26]. Within a GAN, the generator network
is responsible for creating data, and concurrently, the discriminator network
assesses the authenticity of this generated data. Using an adversarial
mechanism, the discriminator is trained to discern between real and fake data,
while the generator aims to produce data that is indiscernible from real data.
GANs have evolved into diverse variants, each enhancing the original concept
for specific purposes. The Conditional GAN (CGAN) introduces conditionality to
direct the generative process with greater precision [68]. In parallel, the
Wasserstein GAN with Gradient Penalty (WGAN-GP) marks a significant stride in
stabilizing the training process, adeptly countering the prevalent issue of
mode collapse with its innovative loss function [69]. The Auxiliary Classifier
GAN (ACGAN) ingeniously integrates an auxiliary classifier into the
discriminator, thereby elevating the fidelity and diversity of the generated
images [70]. The Adversarial Autoencoder (AAE) forges a pathway by blending
AEs with adversarial training, enforcing specialized distributions within the
latent space [71]. The Variational Auto-Encoding Generative Adversarial
Network (VAEGAN) synergizes the structured latent spaces of VAEs with the
superior generation capabilities of GANs [72].  Collectively, GANs excel in
generating high-quality, realistic content and learning data distributions
without explicit modeling. However, they are challenged by training
difficulties, potential for mode collapse, and the generation of nonsensical
outputs.

  * •

Diffusion Models (DMs): Diffusion Model, also known as the score-based
generative model, is a novel type of generative model inspired by non-
equilibrium thermodynamics [27]. Similarly to VAE, DMs aim to learn the
distribution of the original data. By adding noise to the original data, the
data distribution can approach a normal distribution. Through denoising steps,
the noise from the normal distribution is reverted back to data from the
original distribution.  Diffusion models are proficient in generating highly
realistic data, but their long training times and computational intensity
highlight the potential for further efficiency improvements in this emerging
method [73].

Given the powerful generative capabilities, GAI has been deployed in a
multitude of applications, including image and video synthesis, data
augmentation and so on. More recently, the integration of GAI into physical
layer security in wireless communications is a burgeoning field with promising
potential. To be more specific, GAI can play several roles in enhancing
security at the physical layer: 1) Encrypted Communication; 2) Signals
Authentication; 3) Attacks Defense; 4) Anomaly Detection; 5) Adaptive Signal
Processing (Table III).

##  III Communication Confidentiality and Authentication

In wireless communications, the principles of confidentiality and
authentication stand as critical pillars ensuring the security and integrity
of transmitted information [63]. However, cyber threats, such as eavesdropping
and unauthenticated attacks in physical layer, significantly compromise
communication security, leading to unauthorized access and information
breaches [74]. This section provides an overview of employing GAI techniques
to ensure communication confidentiality and authenticity.

###  III-A Secure Communication

TABLE IV: Summary of GAI for Secure Communication in Physical Layer
Blue circles describe the methods; Green correct markers and Red cross markers represent pros and cons respectively. Techniques |  Reference |  Algorithm |  Pros & Cons
---|---|---|---
Encrypted Communication |  [75] |  AE |  ⚫ A flexible wiretap code design for Gaussian wiretap channels under finite blocklength by neural network AEs ✓ Flexibility to trade-off between the BLER and leakage. ✓ Achieve decent performance with simple network structures. ✗ Slightly worse performance than the polar wiretap codes. ✗ Trained with fixed SNR.
|  [28] |  GAN |  ⚫ The GAN architecture to learn non-linearities, memory effects, and non-Gaussian statistics. ✓ Approximate the channel response under different conditions. ✗ Based on a specific distribution dataset.
|  [29] |  SVAE |  ⚫ A DL-based transceiver design for secrecy systems ✓ Novel loss to measure the information leakage ✓ Robustness to the changing environment ✓ Trained in an unsupervised fashion without labeling effort ✗ Limited scalability of the code. ✗ Learning high-dimensional codes is computationally challenging.
Anti-eavesdropping |  [76] |  VAE |  ⚫ A data-driven approach using VAE-based JSCC ✓ A joint source channel coding framework ✓ Hide the sensitive information different from the original signal ✗ The eavesdropper’s channel quality is assumed to be significantly worse.
|  [30] |  VQ-VAE |  ⚫ A JSCC scheme based on VQ-VAE for point-to-point wireless communication ✓ Simulate noise channel effects ✓ Learn joint codewords incorporating the characteristics of the message channel ✗ Highly dependent on training set
|  [77] |  GAN |  ⚫ A GAN inspired approach using DSSS to ensure that a transmitter and receiver can communicate safe ✓ Use low Peak Side Lobe to improve model convergence ✓ Use of multiple spreading codes instead of one shared code ✗ Relatively high reconstruction accuracy of eavesdroppers
|  [31] |  WGAN-GP |  ⚫ A physical layer key generation method based on WGAN-GP AAE ✓ Overcome the difficulty of quantifying the extracted features ✓ Reduce the quantization complexity ✗ The key randomness is related to the interpretability of neural network.

Eavesdropping is a typical attack in physical layer which involves
intercepting and accessing confidential information transmitted over networks
[78]. To improve confidentiality and achieve anti-eavesdropping, secure data
is usually encrypted through various encryption algorithms [79]. However, once
the math problem used for encryption is solved effective, the security of the
encryption method will be seriously compromised. Moreover, several
transitional methods including Error-Correcting Codes (ECCs) [80] suffer from
a dilemma that they cannot achieve the trade-off between the reliability and
data leakage because of the fixed code parameters. GAI methods, particularly
those employing AEs or VAEs, offer enhanced security via generating complex
structures that are difficult to decipher or reverse-engineer.

Figure 2: The overall architecture of the AE-Based [75] and VAE-Based secure
transceiver [29]. Part A demonstrates a wiretap system model with AWGN. Part B
illustrates the whole framework of AE-Based secure transceiver, which is
trained by two loss functions: the mean-squared error between transmitter
messages and reconstructed messages and the mutual information between the
messages and the received symbols by the eavesdropper. In Part C, the VAE-
Based secure transceiver adds additional loss function: KL divergence.

AEs, characterized by its encoder and decoder components, enable the efficient
encoding of information into a compressed, less interpretable format for
transmission. Based on this, the authors proposed a AE-based framework in
[75], which allows a flexible design of finite blocklength wiretap codes (Fig.
2). The operating point with respect to the trade-off between Block Error Rate
(BLER) and information leakage can be changed easily due to the higher
flexibility. In the scenario with Additive White Gaussian Noise (AWGN)
channels as noise [75], all tested AEs perform slightly worse than the polar
wiretap code [81]. The proposed framework achieve a BLER of around 26.2% and a
leakage around 1.46bits while the polar wiretap code achieves a leakage around
1.33bits at a similar BLER of around 26.4% [75]. Even though the performance
is not better than the polar wiretap code, the proposed framework can take the
advantage of the flexibility to trade-off between the BLER and leakage, and
may improve performance with deeper neural networks.

To train a secure communication system based on AEs, loss gradients need to be
passed backward from the output layer of the receiver to the input layer of
the transmitter. However, a practical challenge arises due to the unknown
gradients of the physical channel. This issue is often circumvented by
assuming channel models with known analytic expressions, such as AWGN and
Rayleigh fading channels [82]. However, these models may not accurately
reflect real-world channel conditions. To overcome this limitation, the
authors in [28] proposed a communication channel density estimating GAN,
inspired by BicycleGAN [83]. The proposed method focuses on channels
characterized by a combination of non-linear amplifier distortion, pulse shape
filtering, inter-symbol interference, frequency-dependent group delay,
multipath, and non-Gaussian statistics. They conducted a comparative analysis
of the marginalized probability density functions of the channel with a
trained generator. Through experiments conducted on four different channels,
the results indicate that the proposed model is capable of generating high-
accuracy approximations of the channel [28].

One of the shortcomings of this AE-based framework is that it is trained based
on the fixed Signal-to-Noise Ratio (SNR). When the SNR varies in the testing
phase, their method cannot provide the optimal solution. To address this
issue, the authors in [29] proposed a VAE-based scheme for secrecy systems in
changing environment (Fig. 2). In this framework, they proposed a secure VAE
(SVAE) to perform as a transceiver designed with a loss function which can
measure the information leakage.  Its loss function is specifically designed
to increase the difficulty for eavesdroppers to recover data, thereby meeting
diverse application requirements of the transceiver. In experiments, Bit Error
Rate (BER) is adopted to evaluate the communication quality. Compared with the
AE-based method [75],  the BER of SVAE achieves around $5\times 10^{-6}$ when
SNR is $10$ dB where the BER of AE-based achieves $10^{-2}$ in a perfect CSI
scenario. Moreover, the BER at eavesdropper of SVAE does not decrease and keep
at $0.5$ where AE-based’s BER has marked decrease [29].  With this high BER at
eavesdropper and low BER at legitimate, the eavesdropper cannot recover
correct information and the legitimate can keep a high communication quality.

However, the model in [75] only focuses on channel coding, where source and
channel coding are performed separately, might not be as efficient in dynamic
communication networks. Joint Source Channel Coding (JSCC) can dynamically
adjust the coding strategy based on both the source content and the channel
conditions which is more suitable for dynamic networks [84]. The authors in
[76] proposed a data-driven approach using VAE-based JSCC. The proposed model
aims to minimize the information leakage and emphasises hiding an underlying
sensitive information.  The VAE enables precise control over latent
distributions and practical variational approximation computation, crucial for
calculating information security dynamics in the proposed model. Evaluated on
colored MNIST dataset, the proposed method provides minimally distorted source
transmission with maximum channel capacity [76].

Similarly, the authors of [30] proposed a VQ-VAE [67] based JSCC wireless
communication framework. This framework interprets both channel and source
encoder (ENC) and decoder (DEC) as variational techniques. A notable feature
of the VQ-VAE is the codebook, which facilitates the modeling of noisy
channels in communication. Specifically, noise is represented by codeword
through an index of binary digits to improve generalization [85].  Similarly,
distortion can be injected between the ENC and DEC to emulate a noisy channel,
enhancing the quality of communication. Furthermore, the ENC, DEC, and
codebook are intricately dependent on the dataset and channel conditions
during the training phase [30]. Leveraging this dependency, the framework can
be adapted for secure and private communication applications specifically to
prevent eavesdropping. In such scenarios, an eavesdropper would face
significant challenges in accurately reconstructing the intended message
without prior knowledge of the specific ENC and codebook used.

Beyond directly employing VAEs for encoding transmitted information, [77]
investigated the application of a GAN-inspired model for covert communication
through Direct-Sequence Spread Spectrum (DSSS). This model is designed to
secure communications between two parties, Alice and Bob, by preventing an
eavesdropper, Eve, from detecting  in an AWGN environment. In this setup,
Alice transmits a message to Bob using a spreading code from a shared
codebook.  The GAN-inspired model utilizes the information eavesdropped by Eve
to generate the spreading code used by Alice and Bob. The system is jointly
trained with a combined loss function, aiming to minimize Bob’s reconstruction
error while maximizing Eve’s. Furthermore, spreading sequences with low Peak
Side Lobe can improve model convergence. However, when Eve employs Auto-
Correlation-based Detection techniques, Eve detectd the presence of the DSSS
signal with an accuracy of 70% at -6 dB SNRs or higher [77]. This significant
level of detection accuracy indicates that the proposed method must adopt more
proactive strategies to ensure enhanced security in communications.

Key generation that exploits the unpredictable characteristics of wireless
channels can provide information-theoretic security for communication
confidentiality [86]. By utilizing the unique and unpredictable
characteristics of channels, key generation methods can effectively prevent
eavesdroppers from gaining access to the encrypted data. However, when
directly adopting AEs or VAEs, the unpredictability of the hidden layer output
and the inability to estimate high-dimensional features in advance pose
challenges for applying these methods to key generation in the physical layer
of communication systems. Therefore, a physical layer key generation method
based on WGAN-GP [69] based AAE was proposed in [31]. This model is designed
to efficiently extract features between legitimate nodes in a way that these
features align with a Gaussian distribution. Compared with the Principal
Component Analysis (PCA) method [87], the proposed method can yield higher
security key capacity and a lower key error rate 15% which is lower than PCA
with feedback, and 10% lower than the without feedback PCA. Additionally, the
key generation ratio of this method is much higher than that achieved with PCA
[31].

As summarized in Table IV, due to its ability to learn distributions and
extract features, GAI can significantly improve the security of data
transmission by generating encryption algorithms preventing evolving threats.
However, existing encryption methods [75, 28, 29] mostly depend on specific
dataset reducing the generality of the method. Improving the generalization
ability of the GAI model while maintaining accuracy may be a research
direction in the future. In addition, communication quality depends greatly on
the interpretability of neural networks [31], which is still an open question.

###  III-B Communication Authentication

TABLE V: Summary of GAI for Communication Authentication in Physical Layer
Blue circles describe the methods; Green correct markers and Red cross markers represent pros and cons respectively. Techniques |  Reference |  Algorithm |  Pros & Cons
---|---|---|---
RF Fingerprinting Authentication |  [32] |  GAN |  ⚫ A GAN framework adapted for use within the RF fingerprinting context ✓ Introduce a weakness in conventional AI methods ✓ Augment the train dataset. ✗ Cannot handle fast time-varying information
|  [88] |  GAN |  ⚫ A framework for building a robust system to identify rogue RF transmitters ✓ Exploit transmitter specific “signatures” including I/Q imbalance ✗ Require additional classifier
|  [89] |  GAN |  ⚫ A robust wireless transmitter identification scheme using GAN ✓ Use a multi-classifier to both detect attackers and transmitters ✗ Real wireless channel effect is not reflected
|  [33] |  Triple-GAN |  ⚫ A semi-supervised specific emitter identification using GAN ✓ A semi-supervised classification ✗ Require relatively long training time
CSI Authentication |  [90] |  GAN |  ⚫ The use of GAN and measured MIMO communications channel information to make a decision on Authentication ✓ Effective in a variety of wireless environments ✓ Use adversarial training for the discriminative model ✗ Cannot handle fast time-varying information ✗ Relatively limited performance at low SNR
|  [34] |  CGAN |  ⚫ A method for physical layer authentication using two variations of CGAN ✓ Utilize time-varying CSI as conditional input ✓ Handle stochastic nature of the wireless channel ✗ Not outstanding performance.
CIR Authentication |  [35] |  HVAE-PLA |  ⚫ HVAE algorithm applied for learning industrial wireless channels ✓ Without requiring attackers’ prior channel information ✓ Extract valuable features of high-dimensional CIRs ✗ Require relatively long training time ✗ A trade-off between the involved class number and the authentication performance

In communication networks, safety-critical messages, which are essential for
the safe operation and coordination of systems, are frequently transmitted.
These include vital communications such as collision warnings, speed limit
notifications, and updates on traffic conditions in vehicular networks [91,
92]. To ensure these messages are genuine and trustworthy, implementing an
authentication process is a critical measure to thwart malicious activities.

RF fingerprinting is a technique used to identify and authenticate wireless
devices based on the distinctive characteristics inherent in RF signals. Given
its ability to accurately pinpoint the source of a transmission, RF
fingerprinting is seen as a crucial tool for device authentication and access
control [93]. Recently, several traditional AI methods have been adopted as
the standard approach for RF fingerprinting. In [94], a CNN framework was
proposed to distinguish transmitters by the estimated error present in their
transmitted waveforms. A Long Short-Term Memory (LSTM) network model was
proposed in [95].

However, the authors in [32] revealed a weakness in the training processes of
these approaches that a malicious GAN can be trained to introduce signal
imperfections without modifying the bandwidth or data contents of the signal
to force classifier errors. Then they showed that the classifier, trained by
the augmented dataset with adversarial examples from GAN, can mitigate this
vulnerability. The experiment results demonstrate that the Receiver Operating
Characteristic (ROC) curves with GAN-augmented training has nearly 1 Area
Under the Curve (AUC), where 90% of the networks without GAN perform even
worse than random guessing at 40 dB SNR [32]. Similarly, in [88] the Radio
Frequency Adversarial Learning (RFAL) framework was proposed for building a
robust system to identify rogue RF transmitters by designing and implementing
a GAN. The GAN utilizes the In-phase and Quadrature (IQ) imbalance [93] to
extract unique high-dimensional features from the RF signals . Using the
augmented data from the generator in GAN, a discriminator model can classify
the trusted transmitters and the counterfeit ones with 99.9% accuracy.

Inspired by [88], the authors in [89] proposed a GAN based wireless
transmitter identification scheme. The proposed framework uses a multi-
classifier to both detect malicious attacker and classify trusted transmitter
without any extra classifier.  Once trained, discriminators are employed to
check whether the captured unknown IQ data comes from a corresponding trusted
transmitter. If the label vector, made up of 1s and 0s, shows all 0s, the data
is not from a trusted source, suggesting a high probability of it being
sourced from an attacker. Additionally, the authors in [33] introduced the
Triple-GAN structure [96] to adopt semi-supervised classification. With the
modified structure, the classification accuracy of the proposed framework can
achieve over 90%, only 1% of the training data samples are labeled [33].

Channel State Information (CSI) is an important parameter of a communication
link in the physical layer. By leveraging its unique properties, CSI can be
utilized in an authentication context [97, 92]. Once a transmitter is
initially authenticated by certain methods such as RF fingerprinting, the
receiver maintains this authentication status as long as the variations in the
received CSI remain below a certain threshold compared to the CSI from
previous transmissions. However, attackers can modify various aspects of their
transmission setup, including antenna properties, transmission timing, power
levels, or use reflectors [98]. These alterations enable them to change their
CSI as measured by the receiver.

To address these issues, the author in [90] proposed a GAN based model to
authenticate devices in Multi-Input Multi-Output (MIMO) communication systems.
The proposed model employs adversarial training to improve the authentication
process. The discriminative model at the receiver is trained by a generator
that creates fake CSI samples looking like the authentic samples. Simulation
results show that the discriminator achieves 100% accuracy for SNR greater
than or equal to 10 dB. For SNR less than 10 dB, while the discriminator makes
errors in correctly recognizing legitimate samples, it consistently succeeds
in preventing illegitimate samples from being authenticated [90].

To handle the time-varying CSI in fast-changing environment, the authors in
[34] proposed a CGAN based model combining with LSTM and gated recurrent unit
(GRU) cells. Compared with the method in [90], the proposed model utilizes a
CGAN instead of a conventional one, which can incorporate the previous CSI
elements associated with time as the conditional information (Fig. 3). This
approach allows for a more detailed generation and analysis of CSI data in the
temporal aspect and historical patterns. In experximents, the CGAN-GRU network
typically performed as well as or better than the standalone LSTM or GRU
networks. Especially when mean-square error threshold is -25 dB, all of them
can achieve accuracy at almost 99% [34].

In addition to CSI, the Channel Impulse Response (CIR) is another significant
parameter in wireless communications providing a detailed characterization of
how a wireless signal propagates from the transmitter to the receiver in a
specific environment. In [35], the authors proposed a CIR-based hierarchical
VAE physical-layer authentication (HVAE-PLA) scheme. The HVAE-PLA consists of
an AE module and a VAE module. The AE module is dedicated to extracting the
characteristics of CIR, providing insights into how signals propagate in
specific environments. The VAE module building upon this aims to enhance the
representational capacity of the extracted CIR characteristics.  Compared with
a conventional AI method in [99], the proposed scheme can authenticate the
spoofing nodes in all positions in the static dataset. Moreover, the
simulations show that the proposed method can improve the authentication
performance by 17.18%–69.3% compared to the vanilla AEs and VAEs [35].

Figure 3: Proposed CGAN training architecture in [34]. In Part A, the
conditional information is the previous magnitudes of the CSI elements
associated with time. The output of the discriminator is the probability
value, representing the likelihood from zero to one based on its perception of
whether the sample is fake or authentic. Part B illustrates the system model
structure.

The integration of GAI in communication authentication through specific
information has highlighted GAI’s ability to enhance the uniqueness and
reliability of identifying devices in a network, as summarized in Table V.
However, generative models require relatively long training and inference
times due to their complex structure [33]. When facing fast time-varying
information [32], they have difficulty to infer and adapt to additional new
information in real time. Therefore, pruning the model size and enhancing
model real-time adaptation are urgently needed for security authorization.

##  IV Communication Availability and Resilience

The concepts of communication availability and resilience emerge as
fundamental components to maintain continuous and reliable access for
communication systems [100]. Challenges such as network disruptions and
deliberate cyber attacks can severely impact the availability of digital
communication services, leading to significant downtime and loss of
connectivity [101]. This section aims to explore the integration of advanced
strategies and GAI techniques to ensure the communication availability and
resilience via solving two common cyber attacks: jamming and spoofing in
physical layers.

###  IV-A Anti-jamming Strategy

TABLE VI: Summary of GAI for Anti-jamming Strategy in Physical Layer
Blue circles describe the methods; Green correct markers and Red cross markers represent pros and cons respectively. Techniques |  Reference |  Algorithm |  Pros & Cons
---|---|---|---
Jamming Recognition |  [36] |  GAN |  ⚫ An adversarial machine learning approach launch jamming attacks on wireless communications and a defense strategy ✓ Rely on a small amount of sample data ✓ Do not need any knowledge of transmitter’s algorithm ✗ Limited resilience of the strategy in highly dynamic and unpredictable wireless environments
|  [102] |  GAN |  ⚫ An input-agnostic adversarial attack technique based on GANs and multi-task loss ✓ Quickly craft small imperceptible perturbations ✓ Not depend on the original samples ✗ Just consider certain scenarios
|  [37] |  AC-VAEGAN |  ⚫ A jamming recognition method based on AC-VAEGAN ✓ Stable recognition performance in the case of small samples ✗ Require relatively long training time ✗ Unsuitable for large-scale deployment
|  [103] |  GAN |  ⚫ An algorithm based on GAN TO mine the relationship of the data and complete the missing data ✓ Complete spectrum data in multiple jamming patterns ✗ Less emphasis on real-world testing
Anti-Jamming |  [38] |  GAN |  ⚫ A GAN based spectrum completion network ✓ Complete the partially missing spectrum data ✓ Achieve high reward of the policy ✗ Slightly poor performance in the high missing rate scenario
|  [104] |  GAN |  ⚫ A PU-friendly dynamic spectrum anti-jamming access scheme combining offline training and online deployment ✓ Focus on both PU and SU ✓ Converge fast to the optimal policy ✗ Still slow convergence speed
|  [39] |  ADRLDN |  ⚫ A decision related judgment module between jammer and user based on GAN ✓ Adapt to complex types of jamming ✓ Superior in anti-jamming performance than the current anti-jamming method ✗ Require relatively long training time
|  [105] |  GAN |  ⚫ The secrecy communication in an EH-enabled Cognitive EH-CIoT network with a cooperative jammer ✓ Maximize the system’s secrecy rate while minimizing the SOP ✗ Potential limitations in real-world implementation ✗ complexity of the DRL framework
|  [106] |  GAN |  ⚫ An intelligent jamming and anti-jamming framework to analyze and promote the security of semantic communication ✓ A GAN-like game strategy to reflect the relationship between the semantic jammer and receiver ✗ Not suitable for other multilingual model
|  [107] |  GDNN |  ⚫ A communication model in cognitive radios using machine learning to learn the dynamics of jamming attacks ✓ Adapt to the dynamics of the spectrum ✗ Require relatively long training time

The jamming attack is a vital threat to communication availability at the
physical layer, aimed at disrupting legitimate communications by introducing
noise [108]. Therefore, to ensure communication availability, detecting and
mitigating jamming attacks represents a critical initial defense. There are
several conventional methods employed wireless jamming attacks, including
random and sensing-based jamming [109]. However, with the increasing
integration of machine learning techniques into communication systems, both
legitimate transmitters and malicious jammers leverage machine learning
algorithms to understand the spectrum environment better which introduces new
emerging types of attacks including adversarial attacks.

In [36], the authors present an adversarial learning strategy employing GAN to
facilitate adversarial jamming attacks. This approach enables jammers to
generate synthetic data based on a small number of real data samples. These
synthetic samples are then integrated into the training dataset. Simulation
results indicate that the detection accuracy of a jammer closely approximates,
within 0.19% for misdetection and 3.14% for false alarms, that of a jammer
trained with a larger dataset of real samples collected over a long duration
[36]. Furthermore, based on the attack characteristics, they proposed a
defense strategy for the transmitter, centered on rendering its behavior
unpredictable. This can be achieved by the transmitter intentionally
performing incorrect actions, such as transmitting on a busy channel or
refraining from transmitting on an idle channel, during strategically selected
time slots. Additionally, the authors in [102] proposed an input-agnostic
adversarial attack technique, which adopts GAN to create perturbations in
advance. These pre-generated perturbations can then be efficiently applied to
a variety of incoming signals. Furthermore, this approach has the potential to
substantially aid in the development of classifiers that exhibit robustness
against adversarial jamming attacks.

In the case of small sample datasets, the performance of autonomous feature
extraction and classification of DL will be reduced [110]. Especially in real-
world network communications, it is difficult to obtain enough sample data for
anti-jamming due to the privacy policy and the inadequacy of technical
methods. To generate more realistic data, the authors in [37] proposed a
jamming recognition method based on AC-VAEGAN, which combines the VAEGAN [72]
and ACGAN [70]. In this model, the latent space of a small amount of signal
dataset is obtained by VAE firstly. Then, the datasets will be expanded by
sampling points of the latent space and decoding them. Finally, the
discriminator of the GAN framework is extracted for jamming recognition. In
experiments, when the Jamming-to-Noise Ratio (JNR) is -10dB, the average
correct recognition rate of AC-VAEGAN network is approximately 65%, where the
rate of ACGAN and CNN network is only about 55% [37].

Figure 4:  The overall network structure in [103]. Part A illustrates the
generator, which is designed as an AE comprising a convolution layer, a fully
connected layer, and a de-convolution layer. In Part B, two discriminator
modules are crafted using a convolution network and are optimized to focus on
local and global details, respectively. Part B describes the system model
structure.

Except the scarcity of data, the occurrence of jamming attacks frequently
leads to incomplete data, which hinders the ability of anti-jamming strategies
to discern jamming attacks [36].  To complete the missing information, the
authors in [103] proposed an efficient algorithm based on a GAN focusing on
spectrum waterfall completion, where spectrum waterfall is a thermodynamic
block diagram defining the environmental state [111]. The algorithm can
automatically mine the relationship of the data and complete the missing data
accurately. Different from the noise input in the original GAN, they use the
spectrum waterfall with missing data as generator input, which can limit
generator artistry (Fig. 4). From the corresponding complement results, the
proposed algorithm is better than the method without pre-classification, since
the generator that adds auxiliary information is more targeted to the data.
The accuracy is more than 95%, where the latter is nearly 80% [103].

Figure 5: Overall structure of the proposed anti-jamming spectrum access
scheme in [38]. In Part A, a GAN with the Generator (G) and Discriminator (D)
is trained to complete missing spectrum data using historical data. In Part B,
the generator (G) is implemented as SCN. The CSN utilizes the enriched
spectrum data to assist users in selecting optimal communication channels for
anti-jamming.

Several studies have demonstrated that anti-jamming communications, enhanced
by Deep Reinforcement Learning (DRL) [112], can achieve near-optimal
performance in dynamic and unpredictable environments [113, 114].  In [38],
the authors proposed a framework combining the GAN and DRL.  The proposed
framework consists of two stages: the offline stage and online stage (Fig. 5).
Initially, a GAN is trained to complete missing spectrum data using historical
data. Once the GAN training is complete, the generator is implemented as
Spectrum Completion Network (SCN) during the online stage. Subsequently, with
the augmented spectrum data, a DRL-based Channel Selection Network (CSN) is
employed. The CSN utilizes the enriched spectrum data to assist users in
selecting optimal communication channels for anti-jamming. The performance of
the proposed scheme notably surpasses that of the conventional DRL-based
method in [114], as well as the scheme that combines K-Nearest-Neighbor
Interpolation (KNNI) and DRL [115] in all missing rates. Especially, the
proposed scheme achieves the discounted accumulative reward of 8.3 when the
missing rate is 10%. In comparison, the conventional method scores 4.4, and
the KNNI-DRL combination scores 6.5 [38].

Additionally, the authors in [104] introduced a dynamic spectrum anti-jamming
access scheme in the cognitive radio based network [116] that is friendly to
Primary Users (PU) while also safeguarding Secondary Users (SU) from
indiscriminate jamming attack by jammers. Similar to the scheme in [38], this
proposed framework is divided into two stages: the offline stage and the
online stage where the key difference lies in the GAN model used in the first
stage. Here, the GAN is trained to accurately simulate the Spectrum
Environment (SE), which is considered a Virtual Environment (VE). By pre-
training the Channel Decision Network (CDN) offline in this VE, the SU is
equipped to evade both PU signals and jamming in the actual SE, following the
guidance of the trained CDN. According to the experiments, it takes about 90s
for the proposed scheme to converge to the optimal policy while the CDN
trained in SE from scratch spends about 160s [104].

Existing anti-jamming technologies rely on hidden anti-jamming strategies, but
their performance tends to diminish when facing with more sophisticated or
complex jamming types [117]. In [39], the authors proposed a DRL algorithm
with a double network structure, named ADRLDN, which adopts the hidden anti-
jamming idea and can deal with various types of complex jamming in actual
scenarios. In this framework, they designed a GAN network-based user and
jammer decision-making correlation judgment module. The GAN is trained to fit
the environmental state under known user information, and evaluates whether
the user information is obtained by the jammer. The DRL network is trained to
guarantee the user’s decision not obtained by jammers. In this situation,
there are two key points: compare the fitted environmental state with the real
environmental state; ensure both the generation and the evaluation of the
effect of the network. These happen to be the essential characteristics of GAN
networks. According to the simulation experiment results, ADRLDN is superior
in anti-jamming performance than the current anti-jamming method based on
avoiding the idea (ADRLA) [114] by reducing the probability of users being
jammed by 15%.

As for Cognitive Internet of Things (CIoT), a major challenge is extending the
system’s lifespan. Energy Harvesting (EH) technology is a promising solution
to provide sustainable energy to energy-constrained mobile devices in CIoT
systems [118, 119]. However, EH-CIoT systems encounter significant jamming
attacks due to the wireless channels, which exposes information transmissions
to potential security risks. In [105], the study considered an EH-CIoT system
where the communication security of the SU network is threatened. To enhance
security, the authors propose a DRL algorithm that integrates LSTM and GAN
models. This algorithm aims to maximize the system’s secrecy rate while
minimizing the Secrecy Outage Probability (SOP). The GAN network is utilized
to mitigate the time-varying CSI and the adverse effects of random noise at
the receivers. Simultaneously, the LSTM network is employed for extracting
features from the input environment. The study’s findings reveal that the
convergence speed of the proposed algorithm is significantly faster 1.69 and
3.15 times than that of other algorithms that do not incorporate the GAN model
[105]. The integration of the GAN and LSTM model significantly enhances the
algorithm’s ability to quickly capture environmental information and learn
optimal strategies.

SemCom is a revolutionary way of communicating that helps overcome the
limitations of previous methods by using DL to send necessary information,
which reduces the amount of data sent [120]. With the focus on semantic-level
transmission, new challenges in jamming and anti-jamming arise. Attackers will
aim to create more effective jamming methods to degrade the quality-of-
experience (QoE) for users in communications [121]. In [106], a framework for
intelligent jamming and anti-jamming in semantic communication was proposed
based on the GAN. In the framework, the transmitter sends data with semantic
features, and the receiver tries to understand it correctly while a jammer
tries to mess up this process. The authors designed a GAN model where the
jammer learns to generate disruptive signals, the receiver is trained to
selectively focus on legitimate segments of the incoming data, thereby
enhancing its proficiency in identifying and mitigating semantic jamming.

In jamming attacks, a key factor for their success is the jammer’s ability to
accurately determine the frequency of signal transmission. This capability is
vital for generating jamming noise powerful enough to disrupt the SNR within
the same frequency band. To mitigate jamming attacks, the author in [107]
developed an anti-jamming communication system model based on GANs. The
proposed model employs generator and discriminator integrated with min-max
game theory, to automatically adapt to the dynamics of the spectrum. The
training of the proposed model within this defense mechanism is designed to
mislead jammers, preventing them from effectively targeting the transmission
of data. This strategic deception, rooted in game theory, hinders the jammers’
ability to accurately select time slots for their attacks, leading to
erroneous predictions on classification sources, which in turn prevents
significant transmission losses.

As summarized in Table VI, GAI has demonstrated the effectiveness in
identifying jamming activities and developing suitable anti-jamming
strategies. However, most existing works only consider certain scenarios and
lack real-world testing [102, 103, 106]. Due to the complex architectures, GAI
models are also unsuitable for large-scale deployment [37]. Therefore,
designing a model that can be used in realistic anti-jamming scenarios
especially on a large scale needs to be considered.

###  IV-B Spoofing Defense

TABLE VII: Summary of GAI for Spoofing Defense in Physical Layer
Blue circles describe the methods; Green correct markers and Red cross markers represent pros and cons respectively. Techniques |  Reference |  Algorithm |  Pros & Cons
---|---|---|---
Spoofing Defense |  [40] |  GAN |  ⚫ An approach of spoofing wireless signals by using a GAN ✓ Provide GAN-based model defense mechanism ✗ Limited to simulated environments
|  [122] |  GAN |  ⚫ A DL-based spoofing attack to generate synthetic wireless signals ✓ Detailed analysis and implementation ✗ Not fully explore the potential countermeasures against such attacks
|  [123] |  WGAN-GP |  ⚫ The task of full-band spectral generation in addition to single signal generation ✓ Treat LTE signals as 2D images ✗ Poor performance of generated signals
|  [41] |  CBEGAN |  ⚫ A wireless spoofing attack scheme against the defense mechanism with adversarial DL ✓ Compensate for transmission channel effects via auxiliary channel sensing ✗ Consider specific channel conditions ✗ Limited to simulated environments
|  [124] |  GAN |  ⚫ A GNSS anti-spoofing method based on the idea of confrontation evolution of a GAN ✓ Detect small delay spoofing signals ✓ Extract features of slight differences ✗ The overall performance is not remarkable.
|  [42] |  SJG-GAN |  ⚫ A generation method for spoofing jamming signals ✓ Learn the latent distribution of DSSS signals ✓ Propose a improved Pearson correlation coefficient ✗ Lack real-world testing

Authenticating wireless signals at the physical layer is essential for
ensuring communication resilience. Despite employing numerous features
discussed in Section III, wireless signal spoofing remains a pervasive threat.
In spoofing, attackers insert fake identification information into genuine
communications to join or corrupt the systems [125]. Therefore, it enable
unauthorized access and data manipulation at the physical layer, causing
substantial harm to the communication resilience.

Currently, DL methods have proven effective against simple spoofing attacks.
For example, the study in [40] examines a basic spoofing technique like the
replay attack, which partially replicates original signals.  However,
adversarial spoofing attacks, as discussed in [36], pose a more profound
threat by evading traditional security measures. The research in [40] explored
this issue from both the attacker’s and defender’s perspectives, proposing a
GAN model to create indistinguishable signals. The GAN is trained to emulate
the pattern of intended transmissions which significantly improves the
possibility of a successful attack compared to random signal and replay
attacks, even when node locations vary between training and testing phases
[40]. Moreover, the proposed GAN-based model provides defense mechanism by
using GAI to distinguish and counteract signal spoofing attacks. In [122], the
authors further provided a detailed analysis of the proposed GAN-based
spoofing attack, including its implementation on embedded platforms. This
implementation is carried out on two distinct embedded platforms: an embedded
Graphics Processing Unit (GPU) [126] and a Field-Programmable Gate Array
(FPGA) [127]. The effectiveness of the proposed attack is noteworthy, with a
success probability ranging from 60.6% to 97.8%. However, the technique’s
dependence on real-time compensation for transmission channels causes
considerable overhead. This feature elevates the risk of detection due to an
expanded communication footprint.

Simulating and imitating RF signals is a basic tactic employed by spoofers.
While GAI has demonstrated effectiveness in augmenting short time series
segments, challenges remain in accurately generating RF signals, such as the
length of signals, and radio environments [123]. The authors in [123] explored
the potential of GAN models to accomplish full-band spectral generation for
anti-spoofing attacks. They implement the WGAN-GP model [69] to improve
training stability. Drawing on its proven effectiveness in the image domain,
they utilize spectral representations of OFDM signals called LTE [128],
treating them as 2D images. Nonetheless, the study shows that using GANs to
create long sequences over time is quite challenging. It is harder to capture
small details and features in these long sequences than in the shorter ones
[123].

To overcome the limitations of existing methods, [41] introduces a
controllable wireless spoofing attack scheme that leverages a Conditional
Boundary Equilibrium Generative Adversarial Network (CBEGAN) [129] in
conjunction with auxiliary channel sensing. The CBEGAN network combines with
an AAE, which is a well-established deep neural network architecture for
computer vision-related tasks enabling learning with few samples [71]. It
facilitates more precise and effective spoofing attacks by simulating a
variety of emitters and modulation types. Additionally, the integration of
auxiliary channel sensing effectively compensates for transmission channel
effects. Since it allows the attack model to be trained offline, it
significantly reduces the likelihood of detection by legitimate communication
pairs. Under the same channel conditions, the proposed spoofing attack scheme
reaches a success probability of 85.7%. In contrast, the comparative attack
scheme mentioned in [122] achieves a lower success rate, with a probability of
76.2% [41].

Figure 6:  The overall network structure in [42]. Part A illustrates the SJG-
GAN framework consisting of two parts: signal generation and the high-
performance generated signal selection. In Part B, the process of the high-
performance generated signal selection is shown. It can be split into two
steps: the evaluation and the selection

One sophisticated form of spoofing attack is spoofing jamming, where the
attacker broadcasts analog signals designed to imitate authentic signals. This
can lead to a target receiver obtaining false information instead of the true
data. Due to their long-distance transmission from satellites to receivers,
Global Navigation Satellite System (GNSS) signals are more prone to
disruptions from spoofing jamming attacks [130]. To detect spoofing jamming
attacks, the authors in [124] proposed a spoofing signal detection method
based on the GAN in the acquisition stage, which is one of several phases in
character recognition that also includes preprocessing, feature extraction,
classification, and post-processing [131]. The proposed model specifically
considers the classification of authentic and spoofing signals within the
context of navigation tasks. In this setup, both the training and test
datasets are derived from the GPS receiver code. According to the simulation
results, the successful detection of small-delay spoofing signals is achieved
through the use of adversarial learning within the GAN. Additionally, while
the overall performance of the GAN is comparable to that of the CNN, the GAN
exhibits a slight advantage over the CNN, particularly when the pseudo-code
phase offset is equal to or greater than 0.5 chip [124].

However, Spoofing jamming’s creation is complex and resource-intensive,
requiring extensive prior information. So far, only specific authorized or
civilian systems have successfully executed such attacks [132]. Drawing
inspiration from [40], the authors in [42] introduced a GAN-based approach,
termed Spoofing Jamming Generation (SJG-GAN), for crafting spoofing jamming
attacks (Fig. 6). This model is adept at learning the latent distribution of
DSSS signals and generating a set of synthetic signals. Upon completion of
training, a improved Pearson correlation coefficient is used as an evaluation
metric to select the most aggressive synthetic signals for the DSSS system as
spoofing jamming signals. Notably, the one-dimensional GAN model of SJG-GAN
simplifies the generation process, making it more cost-effective and feasible
for a variety of communication systems, as demonstrated in simulations [40].

In conclusions, GAI models are able to offer sophisticated mechanisms to both
detect and counteract spoofing attacks, as summarized in Table VII. However,
since they are limited to simulated environments, the detection accuracy in
actual scenarios still needs further investigation.

##  V Communication Integrity

Communication integrity in the physical layer of a network involves ensuring
that the data transmitted over a physical medium, such as copper wires, fiber-
optic cables, or wireless signals, is delivered accurately and reliably,
without corruption or alteration [63]. This often requires mechanisms for
anomaly detection or data reconstruction to maintain the fidelity of the data
as it moves from one device to another, thereby preserving the integrity of
communication.

###  V-A Anomaly Detection

TABLE VIII: Summary of GAI for Anomaly Detection in Physical Layer
Blue circles describe the methods; Green correct markers and Red cross markers represent pros and cons respectively. Techniques |  Reference |  Algorithm |  Pros & Cons
---|---|---|---
Score-based Detection |  [133] |  AE |  ⚫ The deep-structure AE neural networks to detect the anomalies of spectrum via time–frequency diagram ✓ The threshold selected to trade-off a balance between the probabilities of false alarms and missed alarms. ✗ Include potential biases in signal data selection
|  [43] |  SAIFE |  ⚫ An AAE-based anomaly detector for wireless spectrum anomaly detection using PSD data ✓ A single model across multiple bands to extract interpretable features ✗ The distribution corresponds more to the latent representations than to the original training samples.
|  [134] |  GAN |  ⚫ A GAN-based system trained on available EM signals to detect unseen types of EM waveforms ✓ The generator consists of two AEs connected in series. ✗ Require relatively long training time
|  [135] |  ResNet-AE |  ⚫ An anomaly detection method based on ResNet-AE ✓ Establish an adaptive decision threshold ✗ Cannot classify the types of anomalies
|  [44] |  $\beta$-VAE |  ⚫ A VAE model uses multivariate normal distribution with a parameter $\beta$ included to the KL divergence term ✓ Investigate the impact of different weightings of the KL divergence ✗ Not specify how to select the optimal value of the $\beta$ coefficient
|  [136] |  AE |  ⚫ The AE neural networks into WSN to solve the anomaly detection problem ✓ Satisfy the demand for limited computational resources ✗ Lack of analysis that extends to large scale sensor networks
Prediction-based Detection |  [137] |  MSGAN |  ⚫ A domain-specific framework consisting of offline training and online inference to detect anomalies in the scenario of industrial robotic sensors ✓ Use an adaptive update strategy during offline training ✗ Lack of analysis that extends to diversity scenarios
|  [45] |  E-GAN |  ⚫ A radio anomaly detection algorithm based on modified GAN ✓ Latent representations are controlled rather than being randomly selected. ✓ Capture the distribution of input samples ✗ Relatively high time complexity
|  [46] |  CGAN |  ⚫ The GAI-based abnormality Detection techniques at the physical layer in CR ✓ Implement a hybrid structure for low- and high-dimensionality data ✗ Limited by signal types tested
|  [138] |  Multiple |  ⚫ The GAI frameworks used to detect anomalies inside the dynamic radio spectrum ✓ A comparative analysis of three deep generative models ✗ Cannot be employed to characterize and classify the anomalous signals
|  [139] |  Multiple |  ⚫ GAI-based anomaly detection methods to detect a set of anomalous activities in several radio band ✓ Three deep generative models are applied to spectral density functions ✗ Need to consider the key applications and proper methods or ensembles of methods to achieve the best performance
Figure 7: The proposed model architecture in [43]. The AAE architecture is
trained in a semi-supervised learning for making the features more
interpretable while the reconstruction is fully unsupervised.

Anomaly detection is a method used to identify and mitigate unexpected
deviations or irregularities in the communication channel [140]. However, due
to the unpredictable characteristics of potential anomalies, it is difficult
to gather enough abnormal data for training samples in traditional AI methods
[141]. Thus, there is an urgent need for an unsupervised, automatic feature-
extraction learning model, such as GAI techniques for anomaly detection [142].
Generally, GAI for anomaly detection can be divided into two cartography:
reconstruction-based [133] and prediction-base detection [45].

Reconstruction-based methods identify anomalies through anomaly scores, which
are usually the reconstruction error in GAI models. In the study [133], the
authors proposed a deep AE-based approach for anomaly detection in the
spectrum. The time-frequency features of preprocessed signal data are utilized
to train the proposed network. To differentiate between normal and anomalous
data, the method applies a threshold to the reconstruction errors,
transforming these errors into a binary outcome. The threshold value is
strategically selected to trade-off a balance between the probabilities of
false alarms and missed alarms, and it is determined as the median of a
sequence of reconstruction errors in this study.

While the model presented in [133] demonstrates effectiveness, it lacks
interpretable feature extraction capabilities, such as signal bandwidth and
position. This limitation necessitates the training of multiple copies of the
model for different frequency bands. Addressing this issues, the authors in
[43] introduced Spectrum Anomaly Detector with Interpretable FEatures (SAIFE),
which is an AAE based model (Fig. 7). SAIFE enables the training of a single
model across multiple bands in an unsupervised manner, thereby eliminating the
need for multiple model instances for different bands. Moreover, the AAE
architecture provides a flexible and robust platform for semi-supervised
learning, enabling the extraction of interpretable features based on Power
Spectral Density (PSD) data [143]. Furthermore, the reconstructed signals are
a key asset for localizing anomalies within the wireless spectrum.
Impressively, the model demonstrates exceptional performance in wireless band
classification, achieving an accuracy close to 100% while only utilizing 20%
labeled samples [43].

Similar to the SAIFE model [43], the study in [134] also designed an anomaly
score based on latent representations. The authors proposed an architecture
for electromagnetic waveform anomaly detection, utilizing a dual AE enhanced
GAN. This design differs from the SAIFE model, as the generator in the
proposed method is composed of two AEs connected in series. These encoders map
original and reconstructed data to the latent space, respectively. The Anomaly
Score is defined with the objective of minimizing the L2 distance between
latent representations for anomaly detection.

In response to the complexity and training overhead of GANs and the low
accuracy of traditional AE networks in anomaly detection of electromagnetic
signals, the authors in [135] proposed a ResNet-AE network model. This model
integrates the encoder and decoder with ResNet architecture and LSTM
architectures for efficient feature mapping and data reconstruction. To
process the anomaly detection results and establish an adaptive decision
threshold, a K-Means classifier with two categories is constructed, using a
random initial clustering center to categorize the anomaly scores. After
iterative clustering, the centers for normal and abnormal signal scores are
determined, and the mean value of these centers is used as the threshold for
anomaly judgment. When applied to radar signal anomaly detection, the proposed
ResNet-AE method achieves a high recognition accuracy, exceeding 85% [135].

To further investigate the impact of different weightings of the KL divergence
in the loss function of VAEs, the authors of [44] proposed an approach for
data anomaly detection using a $\beta$-VAE [144]. This advanced model,
employing a multivariate normal distribution, introduces a coefficient $\beta$
to control the KL term. It allows for a more disentangled representation of
data, where each unit in the latent code is responsive to a single generative
element, enhancing the model’s interpretability and effectiveness. However,
the study does not specify the method for selecting the optimal value of the
$\beta$ coefficient.

While existing methods have proven effective in anomaly detection, they often
involve transmitting large volumes of raw data, resulting in significant
channel interference and energy consumption. To address the substantial demand
for computational resources, the study [136] introduces an AE-based
distributed anomaly detection approach in Wireless sensor network (WSN),
characterized by its simplicity with only three layers. Each sensor in the
proposed approach is equipped with a copy of the AE and is responsible for two
primary tasks, in addition to its regular sensing function. The first task
involves providing the input and output data of the AE to the IoT cloud, which
serves as the training data. This data transfer occurs through a gateway or
cluster head at a significantly lower frequency compared to the sensing rate.
The second task is the execution of anomaly detection, which is conducted
locally at the sensor level. This process is independent of any communication
with other sensors, the gateway, or the IoT cloud, thereby enabling efficient
and autonomous anomaly detection within each sensor unit, offering a more
efficient and autonomous approach to anomaly detection in WSN [136].

Besides reconstruction-based methods, prediction-based methods have also
proven to be effective for anomaly detection, which directly predict the
probability of an anomaly without defining an anomaly score[137, 45, 46, 138].

A primary challenge in physical layer sensing is the large amount of unclean
and irrelevant data collected from sensors, known as data imbalance [145].
This issue often results in traditional AI models misclassifying all samples
as abnormal, further complicating anomaly detection [146]. To tackle the data
imbalance problem, [137] introduces the MSGAN model, a GAN-based data
augmentation strategy specifically designed for sensor anomaly detection. This
model integrates WGAN-GP [69] with a novel adaptive update strategy during
offline training. The adoption of an adaptive update strategy allows the MSGAN
to accelerate training convergence and improve the quality of synthetic
samples.

In SAIFE [43], the distribution captured by AAEs corresponds more to the
latent representations than to the original training samples. To address this
limitation, the study in [45] introduced an Encoder-GAN (E-GAN) structure,
which incorporates an encoder network into the original GAN framework to
reconstruct the spectrogram. By integrating an encoder into the standard GAN,
the latent representations are controlled by the encoder rather than being
randomly selected, which ensures that the generator produces data within the
actual data distribution. Consequently, the E-GAN model is more adept at
capturing the distribution of input samples than the SAIFE. However due to the
convolutional structure in E-GAN, the time complexity of the proposed
algorithm is higher than that of the SAIFE model where a fully-connected
architecture is enough [45].

In [46], a framework integrating Dynamic Bayesian Networks (DBNs) [147] and
GANS was proposed to detect abnormalities. A distinctive aspect of this
approach is the use of a generalized state vector [148], consisting of the
signal feature extracted from the Stockwell Transform (ST) and the
corresponding derivatives, as the input for the model. In the proposed
framework, DBNs are used to learn switching models where each switching
variable can be associated with a different linear dynamic model. This
approach is particularly suited for scenarios involving low-dimensionality
data due to the vocabulary size of switching variables. Conversely, CGAN is
employed for scenarios involving high-dimensionality data. While GANs are
capable of effectively managing a high number of different dynamic models
implicitly, they have a notable limitation: unlike DBNs, GANs cannot manage
uncertainty with probabilistic knowledge [46].

According the results in [46], it is demonstrated that approaches utilizing
generative learning of deep features yield superior results in anomaly
detection when compared to conventional techniques, particularly the
Cyclostationary Feature Detector (CFD) [149]. Therefore, the authors in [138]
conducted a comparative analysis of three deep generative models: the CGAN,
the ACGAN, and the VAE for spectrum anomaly detection in the millimeter Wave
(mmWave) communications. Tested on a real dataset collect by The National
Instruments mmWave Transceiver System [138], all three models demonstrated
commendable performance in anomaly detection, particularly the AC-GAN. The ROC
curves from these tests confirmed that these models have a high probability of
detection while maintaining a low false alarm rate. Furthermore, the VAE model
demonstrates more efficient computational performance in both the training and
testing processes compared to the other two networks.

Similarly, the authors in [139] explored a range of generative model
approaches, including U-Net WGAN, ResNet WGAN, and ResNet VAE applied to
spectral density functions. The anomaly scoring mechanism employed varies with
the model: binary cross-entropy loss is used between the input and
reconstruction for U-Net WGAN and ResNet VAE, while mean-squared error loss is
applied for ResNet WGAN. For comparison and validation, three well-known
anomaly detection methods are used as baselines: Isolation Forest [150], One-
class SVM [151], and fAnoGAN [152]. The results demonstrate excellent
performance of these generative models compared to traditional baseline
approaches for various types of anomalies. In particular, the Unet GAN
achieves the highest average in four out of the five metrics [139].

As summarized in Table VIII, GAI models showcase superior performance in
anomaly detection within complicated feature data than traditional AI models.
However, some methods cannot be employed to characterize and classify the
anomalous signals [135, 138], which holds critical importance for the
subsequent maintenance and security of network equipment. Consequently, future
research should concentrate on creating advanced GAI models capable of
detecting and classifying various anomalous signals.

###  V-B Data Reconstruction

TABLE IX: Summary of GAI for Data Reconstruction in Physical Layer
Blue circles describe the methods; Green correct markers and Red cross markers represent pros and cons respectively. Techniques |  Reference |  Algorithm |  Pros & Cons
---|---|---|---
Data Reconstruction |  [47] |  SARGAN |  ⚫ A GAN network to recover this missing spectral information ✓ Not require any information of the missing band locations ✓ All computational complexity is at the training phase. ✗ The requirement for extensive training data
|  [48] |  VAE-GAN |  ⚫ A VAE-GAN-based method for reconstructing DSSS signals ✓ Avoid complex parametric analysis of the signal ✓ Integrate DRSNs and self-attention in VAE-GAN ✗ Unsatisfactory effect in low SNR
|  [153] |  MTS-GAN |  ⚫ A high-precision reconstruction method for electromagnetic environment data based on MTS-GAN ✓ Use the GRUI to simulate time irregularities ✓ High accuracy and convergence speed ✗ The specific requirements for training and implementing
|  [154] |  VAE |  ⚫ Investigate the performance of VAEs and compare the results with standard AEs ✓ Use SSIM metric instead of the peak signal-to-noise ratio ✗ Limited in terms of the variety of noise models
|  [49] |  CDDM |  ⚫ A channel denoising diffusion models for wireless communications to eliminate the channel noise ✓ Eliminate the channel nosie under Rayleigh fading channel and AWGN channel ✗ Relatively long sampling time

Data reconstruction focuses on retrieving the original signal or information
from corrupted or incomplete datasets [155]. This process involves various
techniques to restore or approximate the original data, aiming to overcome the
issues caused by interference and noise.

Traditional reconstruction methods, based on sparse representation and low-
rank matrix completion [156], assume that both full-spectrum data and their
corrupted counterparts are sparsely represented with a full-spectrum and a
gapped-spectrum dictionary, respectively. Therefore, both representations are
similarly sparse and share identical sparse codes. Consequently, these
reconstruction methods lack the ability and robustness to distinguish closely
situated targets at high resolution accurately without prior knowledge of the
missing frequency bands. However, GAI models excel in learning complex data
patterns, effectively reducing the dependency on prior knowledge of missing
frequency bands. Moreover, GAI models leverage their advanced learning
capabilities to data gaps, offering a more robust and flexible approach to
signal reconstruction.

In [47], the authors introduced a GAN framework named SARGAN, designed to
reconstruct missing spectral information in Ultra-wideband (UWB) radar systems
across multiple frequency bands. Specifically, SARGAN focuses on recovering
Synthetic Aperture Radar (SAR) data [157]. To train the GAN model, the model
uses numerous data pairs, each comprising an uncorrupted scene and its
frequency-corrupted version. The corrupted datasets are simulated by removing
random frequency bands from the original data. A significant advantage over
conventional spectral recovery methods is that the proposed model does not
need any prior knowledge of the missing data. This is particularly beneficial
in unpredictable scenarios including battlefield conditions, where jamming and
interference can occur unexpectedly. The simulation results show that the
recovered signals using SARGAN achieve an average gain of over 18 dB in SNR,
even when up to 90% of the operating spectrum is missing.

Compared to radar data, DSSS signals possess more complex structures which
makes it challenging to characterize accurately the properties of a target
signal. To extract more properties such as Pseudonoise (PN) sequence [158], a
method based on VAE-GAN [72] for reconstructing DSSS signals was proposed in
[48]. By integrating VAE and GAN, the encoder provides the generator with a
loss function that measures the discrepancy between real and generated data.
Furthermore, the proposed framework incorporates a Deep Residual Shrinkage
Network (DRSN) [159] and a self-attention mechanism [160] into the encoder and
discriminator. The DRSNs are effective in minimizing redundant information in
the collected signal, particularly noise-induced redundancy. Meanwhile, the
self-attention mechanism facilitates the establishment of long-distance
dependencies within the input sequences. However, while the proposed model is
adaptable to PN sequences with varying code lengths, its performance in low
SNR environments significantly diminishes. Particularly, when the SNR falls
below 13 dB, there is a sharp decline in the model’s performance [48].

Figure 8: The MTS-GAN data completion network structure [153]. The generator
network is built by the gated loop unit GRUI for data interpolation. GRUI can
simulate time irregularities allowing for more accurate extraction of the
distribution characteristics of time-frequency signal data.

To improve the precision of reconstructing electromagnetic environment data,
the authors in [153] developed a high-precision method using a Multi-Component
Time Series Generation Adversarial Network (MTS-GAN). This approach
effectively utilizes multivariate time series data to better capture the
correlations between the time and frequency domains of electromagnetic data
(Fig. 8). A key component of this method is the use of a Gate Recursive Unit
(GRUI), which simulates time irregularities. The GRUI allows for more accurate
extraction of the distribution characteristics of time-frequency signal data
and reduces the impact of random losses in time series. The proposed method
achieves high accuracy and ensures rapid convergence and iterative
optimization speeds with the ’Qingdao Offshore Measurement Data Set’ [153].

Besides GANs, VAEs are recognized as another powerful GAI model for effective
data reconstruction. In [154], a VAE-based model was proposed for data
construction in noisy channels. The AE and VAE models are particularly
effective in regularizing the latent space distribution, a feature that is
highly beneficial in data reconstruction with Gaussian noise channels.  The
VAE’s regularized latent space facilitates accurate decoding by the decoder,
thus improving performance in noisy settings. Consequently, when evaluated on
the STL10 dataset [161] using the Structural Similarity Index (SSIM) metric
[162], the VAE-based method demonstrates a smoother output attributed to its
inherent structure.

DMs have garnered attention in the field of wireless communications due to
their inherent ability to progressively remove noise, especially in aiding
receivers to mitigate channel noise. In response to this potential, the study
[49] introduced Channel Denoising Diffusion Models (CDDM) specifically
designed for wireless communications. CDDM aims to leverage the noise
reduction properties of DMs to enhance the quality and reliability of signal
reception in wireless communication channels. CDDM is trained using a
specialized noise schedule specifically adapted to the wireless channel,
enabling the effective elimination of channel noise through sampling
algorithm. The training algorithm for the combined CDDM and JSCC system is
structured into three distinct stages. In the first and last stages, the JSCC
encoder and decoder are trained to minimize the reconstruction error. The
second stage involves fixing the parameters of the JSCC encoder, thereby
allowing the CDDM to learn the distribution of latent representations. This
stage utilizes a noise schedule that closely simulates the distribution of
channel noise, rendering the CDDM adaptable to a variety of channel
conditions. The results demonstrate that systems incorporating CDDM
consistently outperform those without CDDM across all SNR regimes, under both
AWGN and Rayleigh fading channels. Notably, under an AWGN channel and a
Rayleigh fading channel at 20 dB SNR, the CDDM achieves 0.49 dB gain and 1.06
dB gain, respectively [49].

The applications of GAI for data reconstruction in Table IX showcase its
remarkable ability to process and regenerate missing or corrupted data,
ensuring communication integrity. However, the performance is still limited in
low SNR scenarios [48]. Therefore, proposing more accurate models in high
noise situations is a future direction.

##  VI Future Research Direction

Despite its impressive capabilities in complex data feature extraction,
reconstruction, and enhancement, the applications of GAI in physical layer
security are still in its early stages. This section aims to explore the open
issues and research directions related to the integration of GAI in physical
layer security.

###  VI-A Model Improvements

Enhancing physical layer security necessitates models that significantly
advance in terms of robustness and efficiency requiring model improvements. By
incorporating advanced neural network architectures, GAI systems can learn to
simulate and counteract time related attack patterns more effectively [163].
Enhancements in adversarial training techniques will also enable GAI models to
better mitigate potential vulnerabilities [164].  Moreover, GAI-aided
information encryption may be further explored in conjunction with the near-
field beam focusing via Extremely large-scale multiple-input-multipleoutput
(XL-MIMO) that exploits the propagation characteristics of both distance and
direction. The latter enables to focus the transmitted signal energy onto an
intended user, so as not to induce information leakage to eavesdroppers. This
certainly enhances the physical layer security for emerging 6G Wireless
equipped with XL-MIMO [165].

###  VI-B Multi-scenario Deployment

As the deployment of GAI in physical layer security, its application across
various scenarios emerges as a critical area of focus. The intricate
architecture of GAI poses challenges for its implementation on edge devices,
often requiring the transmission of additional data [136]. Incorporating
distributed deployment strategies, GAI can efficiently leverage edge computing
capabilities, thus minimizing latency and reducing the need for extensive data
transmission by processing information closer to its source [166].
Furthermore, the Mixture of Experts (MoE) model can dynamically assign tasks
to specialized sub-models or ‘experts’ [167]. It presents a promising avenue
for enhancing the adaptability and efficiency of GAI in addressing the
multifaceted and intricate scenarios encountered in physical layer security.
Exploring the integration of the MoE model with GAI to leverage the strengths
of both approaches is a noteworthy direction for future research.

###  VI-C Resource-Efficient Optimization

Compared with traditional AI, GAI usually requires more resources for training
and inference due to its complex mission objectives. It causes serious burden
and impact on the normal process operation of the device, especially for
devices with limited resources such as mobile phones. Therefore, future
directions should emphasize the development of lightweight GAI models that can
operate with minimal computational resources while maintaining high security
standards [168]. For instance, adapting model pruning techniques to remove
unnecessary parameters from GANs without compromising their ability to
generate or discriminate can significantly reduce the computational load
[169]. Additionally, exploring federated learning approaches could
decentralize the training process, allowing GAIs to learn from diverse
datasets across multiple devices while ensuring data privacy and reducing the
need for centralized, powerful computing resources [170]. These strategies
promise to enhance the scalability of GAIs in securing the physical layer and
ensure their applicability in resource-constrained environments including IoT
devices and edge computing platforms, where security and efficiency are
paramount.

###  VI-D Secure SemCom

”GAI-aided Secure SemCom” is certainly a vital future research direction. The
task-oriented SemCom aims at minimizing the transmission overhead in resource-
constrained networks, such as AI-native wireless networks [51]. Additionally,
it focuses on performing a given task properly with the aid of GAI as well as
knowledge base at both ends, even though the reconstructed data is not exactly
same as the original data [120]. Consequently, the performance metric
transitions from bit-level accuracy including BER, to the degree of task
fulfillment within a specified QoE value, given the GAI with knowledge base is
shared between the transceiver. Therefore, this paradigm shift necessitates a
reevaluation of GAI model design criteria within SemCom, focusing on task
fulfillment levels facilitated by the synergistic use of GAI and a shared
knowledge base in physical layer security [121].

##  VII Conclusion

This paper has presented a comprehensive survey on the applications of GAI in
physical layer security, attributed to its remarkable capabilities in
extracting, reconstructing, and enhancing complex data features. It introduced
the background of GAI, encompassing its architecture, classification, and
foundational models. Subsequently, it explored various security properties
such as communication confidentiality, authentication, availability,
resilience, and integrity. Finally, it highlighted crucial future research
directions for generative AI in physical layer security, which underscores the
potential of GAI to further enhance security measures, demonstrating its vital
role in safeguarding communication networks against evolving security threats.

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