Sum Secrecy Rate Maximization for Full-Duplex Two-Way Relay Networks Using Alamouti-based Rank-Two Beamforming

TL;DR

This paper proposes an optimization framework for maximizing the sum secrecy rate in full-duplex two-way relay networks using Alamouti-based rank-two beamforming and artificial noise, ensuring secure communication against eavesdroppers.

Contribution

It introduces a novel joint optimization method for beamforming and artificial noise in full-duplex relay networks, proving the tightness of SDR and convergence of an inexact MM algorithm.

Findings

SDR is tight for the SSRM problem, with no loss in optimality.

The inexact MM method guarantees convergence to a stationary point.

Simulation results demonstrate the effectiveness of the proposed approach.

Abstract

Consider a two-way communication scenario where two single-antenna nodes, operating under full-duplex mode, exchange information to one another through the aid of a (full-duplex) multi-antenna relay, and there is another single-antenna node who intends to eavesdrop. The relay employs artificial noise (AN) to interfere the eavesdropper's channel, and amplify-forward (AF) Alamouti-based rank-two beamforming to establish the two-way communication links of the legitimate nodes. Our problem is to optimize the rank-two beamformer and AN covariance for sum secrecy rate maximization (SSRM). This SSRM problem is nonconvex, and we develop an efficient solution approach using semidefinite relaxation (SDR) and minorization-maximization (MM). We prove that SDR is tight for the SSRM problem and thus introduces no loss. Also, we consider an inexact MM method where an approximately but computationally cheap MM solution update is used in place of the exact update in conventional MM. We show that this inexact MM method guarantees convergence to a stationary solution to the SSRM problem. The effectiveness of our proposed approach is further demonstrated by an energy-harvesting scenario extension, and by extensive simulation results.