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Joint secure AF relaying and artificial noise optimization: A penalized difference-of-convex programming framework

Joint secure AF relaying and artificial noise optimization: A penalized difference-of-convex programming framework
Joint secure AF relaying and artificial noise optimization: A penalized difference-of-convex programming framework
Owing to the vulnerability of relay-assisted communications, improving wireless security from a physical layer signal processing perspective is attracting increasing interest. Hence we address the problem of secure transmission in a relay-assisted network, where a pair of legitimate user equipments (UEs) communicate with the aid of a multiple-input multiple output (MIMO) relay in the presence of multiple eavesdroppers (eves). Assuming imperfect knowledge of the eves’ channels, we jointly optimize the power of the source UE, the amplify-and-forward (AF) relaying matrix and the covariance of the artificial
noise (AN) transmitted by the relay, in order to maximize the received signal-to-interference-plus-noise ratio (SINR) at the destination, while imposing a set of robust secrecy constraints. To tackle the resultant non-convex optimization problem with tractable complexity, a new penalized difference-of-convex (DC)
algorithm is proposed, which is specifically designed for solving a class of non-convex semidefinite programs (SDPs). We show how this penalized DC framework can be invoked for solving our robust secure relaying problem with proven convergence. In addition, to benchmark the proposed algorithm, we subsequently propose a semidefinite relaxation (SDR)-based exhaustive search
approach, which yields an upper bound of the secure relaying problem, however, with significantly higher complexity. Our simulation results show that the proposed solution is capable of ensuring the secrecy of the relay-aided transmission and significantly improving the robustness towards the eves’ channel uncertainties as compared to the non-robust counterparts. It is
also demonstrated the penalized DC-based method advocated yields a performance close to the upper bound.
2169-3536
10076-10095
Yang, Jiaxin
443983a2-c0bc-4fed-b745-259d53dc8290
Li, Qiang
411fba27-9768-49a4-b313-b96f4004587f
Cai, Yunlong
44a85b9f-185b-4078-aecd-02df90f5eab6
Zou, Yulong
0359c94b-b989-448a-8164-da4047c4823f
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Champagne, Benoit
34637814-cef4-4177-b5fd-d748742be072
Yang, Jiaxin
443983a2-c0bc-4fed-b745-259d53dc8290
Li, Qiang
411fba27-9768-49a4-b313-b96f4004587f
Cai, Yunlong
44a85b9f-185b-4078-aecd-02df90f5eab6
Zou, Yulong
0359c94b-b989-448a-8164-da4047c4823f
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Champagne, Benoit
34637814-cef4-4177-b5fd-d748742be072

Yang, Jiaxin, Li, Qiang, Cai, Yunlong, Zou, Yulong, Hanzo, Lajos and Champagne, Benoit (2016) Joint secure AF relaying and artificial noise optimization: A penalized difference-of-convex programming framework. IEEE Access, 4, 10076-10095. (doi:10.1109/ACCESS.2016.2628808).

Record type: Article

Abstract

Owing to the vulnerability of relay-assisted communications, improving wireless security from a physical layer signal processing perspective is attracting increasing interest. Hence we address the problem of secure transmission in a relay-assisted network, where a pair of legitimate user equipments (UEs) communicate with the aid of a multiple-input multiple output (MIMO) relay in the presence of multiple eavesdroppers (eves). Assuming imperfect knowledge of the eves’ channels, we jointly optimize the power of the source UE, the amplify-and-forward (AF) relaying matrix and the covariance of the artificial
noise (AN) transmitted by the relay, in order to maximize the received signal-to-interference-plus-noise ratio (SINR) at the destination, while imposing a set of robust secrecy constraints. To tackle the resultant non-convex optimization problem with tractable complexity, a new penalized difference-of-convex (DC)
algorithm is proposed, which is specifically designed for solving a class of non-convex semidefinite programs (SDPs). We show how this penalized DC framework can be invoked for solving our robust secure relaying problem with proven convergence. In addition, to benchmark the proposed algorithm, we subsequently propose a semidefinite relaxation (SDR)-based exhaustive search
approach, which yields an upper bound of the secure relaying problem, however, with significantly higher complexity. Our simulation results show that the proposed solution is capable of ensuring the secrecy of the relay-aided transmission and significantly improving the robustness towards the eves’ channel uncertainties as compared to the non-robust counterparts. It is
also demonstrated the penalized DC-based method advocated yields a performance close to the upper bound.

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Accepted/In Press date: 12 November 2016
e-pub ahead of print date: 15 November 2016

Identifiers

Local EPrints ID: 433323
URI: http://eprints.soton.ac.uk/id/eprint/433323
ISSN: 2169-3536
PURE UUID: ce6c7b0e-9b69-485b-a6c4-6e722b7978d5
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 14 Aug 2019 16:30
Last modified: 18 Mar 2024 02:36

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Contributors

Author: Jiaxin Yang
Author: Qiang Li
Author: Yunlong Cai
Author: Yulong Zou
Author: Lajos Hanzo ORCID iD
Author: Benoit Champagne

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