Robust Sum Secrecy Rate Optimization for MIMO Two-way Full Duplex Systems

TL;DR

This paper develops a robust optimization framework for maximizing the sum secrecy rate in MIMO full-duplex two-way systems, accounting for imperfect eavesdropper CSI and employing convex optimization techniques.

Contribution

It introduces a novel moment-based channel uncertainty model and a convex optimization approach for secure MIMO full-duplex communication with imperfect CSI.

Findings

The proposed method achieves higher secrecy rates under channel uncertainties.

Convex reformulation enables efficient optimization of transmit strategies.

Simulation validates robustness and effectiveness of the approach.

Abstract

This paper considers multiple-input multiple-output (MIMO) full-duplex (FD) two-way secrecy systems. Specifically, both multi-antenna FD legitimate nodes exchange their own confidential message in the presence of an eavesdropper. Taking into account the imperfect channel state information (CSI) of the eavesdropper, we formulate a robust sum secrecy rate maximization (RSSRM) problem subject to the outage probability constraint of the achievable sum secrecy rate and the transmit power constraint. Unlike other existing channel uncertainty models, e.g., norm-bounded and Gaussian-distribution, we exploit a moment-based random distributed CSI channel uncertainty model to recast our formulate RSSRM problem into the convex optimization frameworks based on a Markov's inequality and robust conic reformulation, i.e., semidefinite programming (SDP). In addition, difference-of-concave (DC) approximation is employed to iteratively tackle the transmit covariance matrices of these legitimate nodes. Simulation results are provided to validate our proposed FD approaches.