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Review
. 2022 May 9;22(9):3589.
doi: 10.3390/s22093589.

A Review of Fundamental Optimization Approaches and the Role of AI Enabling Technologies in Physical Layer Security

Affiliations
Review

A Review of Fundamental Optimization Approaches and the Role of AI Enabling Technologies in Physical Layer Security

Mulugeta Kassaw Tefera et al. Sensors (Basel). .

Abstract

With the proliferation of 5G mobile networks within next-generation wireless communication, the design and optimization of 5G networks are progressing in the direction of improving the physical layer security (PLS) paradigm. This phenomenon is due to the fact that traditional methods for the network optimization of PLS fail to adapt new features, technologies, and resource management to diversified demand applications. To improve these methods, future 5G and beyond 5G (B5G) networks will need to rely on new enabling technologies. Therefore, approaches for PLS design and optimization that are based on artificial intelligence (AI) and machine learning (ML) have been corroborated to outperform traditional security technologies. This will allow future 5G networks to be more intelligent and robust in order to significantly improve the performance of system design over traditional security methods. With the objective of advancing future PLS research, this review paper presents an elaborate discussion on the design and optimization approaches of wireless PLS techniques. In particular, we focus on both signal processing and information-theoretic security approaches to investigate the optimization techniques and system designs of PLS strategies. The review begins with the fundamental concepts that are associated with PLS, including a discussion on conventional cryptographic techniques and wiretap channel models. We then move on to discuss the performance metrics and basic optimization schemes that are typically adopted in PLS design strategies. The research directions for secure system designs and optimization problems are then reviewed in terms of signal processing, resource allocation and node/antenna selection. Thereafter, the applications of AI and ML technologies in the optimization and design of PLS systems are discussed. In this context, the ML- and AI-based solutions that pertain to end-to-end physical layer joint optimization, secure resource allocation and signal processing methods are presented. We finally conclude with discussions on future trends and technical challenges that are related to the topics of PLS system design and the benefits of AI technologies.

Keywords: AI techniques; information theory; optimization approaches; physical layer security; resource allocation; signal processing techniques.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Generic system model of an Alice–Bob–Eve channel.
Figure 2
Figure 2
A system model for a MIMO wiretap channel.
Figure 3
Figure 3
A system model for a broadcast wiretap channel.
Figure 4
Figure 4
A system model for a multiple-access channel.
Figure 5
Figure 5
Secure communication using an interference channel model.
Figure 6
Figure 6
A system model for a wiretap relay channel in the first time slot.
Figure 7
Figure 7
A system model for a wiretap relay channel in the second time slot.
Figure 8
Figure 8
Secure transmission strategies to improve the security and robustness of physical layer designs.
Figure 9
Figure 9
An illustration of multi-dimensional security and resource management within a multi-service wireless network.
Figure 10
Figure 10
An illustration of a MIMO channel autoencoder.
Figure 11
Figure 11
An illustration of downlink resource allocation for a multi-service network.

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