End-to-End Learning of Secure Wireless Communications: Confidential Transmission and Authentication

Sun, Zhuo; Wu, Hengmiao; Zhao, Chenglin; Yue, Gang

doi:10.1109/mwc.001.2000005

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…The main drawback of the study is that Eve is assumed to have more noise than Bob. Another secure AE model is proposed in [296]. Similar to [295], the authors consider the design of a secure loss function based on cross-entropy.…”

Section: Anti-eavesdropping Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

IEEE Open J. Commun. Soc.

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Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising physical layer concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output systems, sophisticated multi-carrier waveform designs, reconfigurable intelligent surface-empowered communications, and physical layer security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DLbased multiple-input multiple-output by sharing user-friendly code snippets, which might be useful for interested readers.

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Section: Anti-eavesdropping Solutionsmentioning

confidence: 99%

“…Similar to [295], the authors consider the design of a secure loss function based on cross-entropy. As an addition, [296] also implements an authentication method into the AE.…”

Section: Anti-eavesdropping Solutionsmentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

“…The main drawback of the study is that Eve is assumed to have more noise than Bob. Another secure AE model is proposed in [284]. Similar to [283], the authors consider the design of a secure loss function based on cross-entropy.…”

Section: Anti-eavesdropping Solutionsmentioning

confidence: 99%

“…Similar to [283], the authors consider the design of a secure loss function based on cross-entropy. As an addition, [284] also implements an authentication method into the AE.…”

Section: Anti-eavesdropping Solutionsmentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

View full text Add to dashboard Cite

Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use-cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer (PHY) methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising PHY concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output (MIMO) systems, sophisticated multi-carrier (MC) waveform designs, reconfigurable intelligent surface (RIS)-empowered communications, and PHY security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based MIMO by sharing user-friendly code snippets, which might be useful for interested readers.

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“…A major challenge comes from the increasing complexity in wireless systems, i.e., the chain of transmitter, channel and receiver. The two main approaches currently used to describe and optimize the system parameters are: i) model-based approach (e.g., using communication theory) where end-to-end communication is modelled as a set of blocks and each block can be parameterized and optimized independently, and, ii) model-free based approach (e.g., using machine learning techniques) where the whole system can be modelled and optimized as a single block [50]. The former approach is typically static, and, hence, performs well in stable environments.…”

Section: Location-based Authentication and Rf Fingerprintingmentioning

confidence: 99%

Physical Layer Security - from Theory to Practice

Mitev¹,

Pham²,

Chorti³

et al. 2022

Preprint

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<p>A large spectrum of technologies are collectively dubbed as physical layer security (PLS), ranging from wiretap coding, secret key generation (SKG), authentication using physical unclonable functions (PUFs), localization / RF fingerprinting, anomaly detection monitoring the physical layer (PHY) and hardware. Despite the fact that the fundamental limits of PLS have long been characterized, incorporating PLS in future wireless security standards requires further steps in terms of channel engineering and pre-processing. Reflecting upon the growing discussion in our community, in this critical review paper, we ask some important questions with respect to the key hurdles in the practical deployment of PLS in 6G, but also present some research directions and possible solutions, in particular our vision for context-aware 6G security that incorporates PLS. </p>

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End-to-End Learning of Secure Wireless Communications: Confidential Transmission and Authentication

Cited by 13 publications

References 15 publications

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Physical Layer Security - from Theory to Practice

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