Physical Layer Security Beamforming Design via Deep Unfolding
Bilbao, Iñigo; Nguyen, Nhan T.; Moya Osorio, Diana P.; Tapio, Visa; Juntti, Markku; Iradier, Eneko; Montalbán, Jon; Angueira, Pablo (2024-08-12)
IEEE
12.08.2024
I. Bilbao et al., "Physical Layer Security Beamforming Design via Deep Unfolding," 2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), Madrid, Spain, 2024, pp. 251-256, doi: 10.1109/MeditCom61057.2024.10621251
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202412097103
https://urn.fi/URN:NBN:fi:oulu-202412097103
Tiivistelmä
Abstract
Physical layer security (PLS) relies on avoiding attacks from a physical layer perspective. Beamforming has shown to be an effective alternative for this purpose, allowing the concentration of the resources in the legitimate user. This paper investigates analog beamforming in a massive multiple-input-multiple-output system with an eavesdropper and a jammer. To ensure secure communications, we formulate the secrecy rate maximization problem under the constant modulus constraint of the analog beamforming coefficients. The problem is highly challenging due to the non-convexity and complicated objective function. To overcome the challenges, we propose a deep unfolding architecture that leverages the learning capability of deep neural networks to improve the convergence of the projected gradient ascent (PGA) optimizer. Simulation results show that the proposed deep unfolding beamforming designoffers a substantial gain of 33.33% in secrecy rate and a 70% reduction in complexity and run-time concerning the conventional PGA scheme.
Physical layer security (PLS) relies on avoiding attacks from a physical layer perspective. Beamforming has shown to be an effective alternative for this purpose, allowing the concentration of the resources in the legitimate user. This paper investigates analog beamforming in a massive multiple-input-multiple-output system with an eavesdropper and a jammer. To ensure secure communications, we formulate the secrecy rate maximization problem under the constant modulus constraint of the analog beamforming coefficients. The problem is highly challenging due to the non-convexity and complicated objective function. To overcome the challenges, we propose a deep unfolding architecture that leverages the learning capability of deep neural networks to improve the convergence of the projected gradient ascent (PGA) optimizer. Simulation results show that the proposed deep unfolding beamforming designoffers a substantial gain of 33.33% in secrecy rate and a 70% reduction in complexity and run-time concerning the conventional PGA scheme.
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