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Journal Articles IEEE Transactions on Information Forensics and Security Year : 2021

On Maximizing the Sum Secret Key Rate for Reconfigurable Intelligent Surface Assisted Multiuser Systems

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Wei Xu
Marco Di Renzo

Abstract

Channel reciprocity-based key generation (CRKG) has recently emerged as a new technique to address the problem of key distribution in wireless networks. However, as this approach relies upon the characteristics of fading channels, the corresponding secret key rate may be low when the communication link is blocked. To enhance the applicability of CRKG in harsh propagation scenarios, this paper introduces a novel multiuser key generation scheme, which is referred to as RIS-assisted multiuser key generation (RMK) that leverages the reconfigurable intelligent surface (RIS) technology for appropriately shaping the environment and enhancing the sum secret key rate between an access point and multiple users. In the RMK scheme, an RIS-induced channel, rather than the direct channel, serves as the key source. We derive a general closed-form expression of the secret key rate and optimize the configuration of the RIS to maximize the sum secret key rate over independent and correlated fading channels in the presence of multiple users. In the presence of independent fading, we introduce a low-complexity algorithm based on the Karush-Kuhn-Tucker (KKT) condition. In the presence of correlated fading, the optimization problem is non-convex and challenging to solve. To tackle it, we propose a new optimization algorithm based on the semi-definite relaxation (SDR) and successive convex approximation (SCA) methods. Simulation results demonstrate that the proposed RMK scheme outperforms existing RIS-assisted algorithms and achieves a near-optimal sum secret key rate over independent and correlated fading channels.
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Dates and versions

hal-03506039 , version 1 (01-01-2022)

Identifiers

Cite

Guyue Li, Chen Sun, Wei Xu, Marco Di Renzo, Aiqun Hu. On Maximizing the Sum Secret Key Rate for Reconfigurable Intelligent Surface Assisted Multiuser Systems. IEEE Transactions on Information Forensics and Security, 2021, pp.1-1. ⟨10.1109/TIFS.2021.3138612⟩. ⟨hal-03506039⟩
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