Sum secrecy rate maximization for full-duplex two-way relay networks

Abstract

Consider a full-duplex two-way relay network, where two legitimate nodes simultaneously transmit and receive confidential information through a full-duplex multiantenna relay, in the presence of an eavesdropper. To secure the communications, an artificial-noise (AN)-aided amplify-and-forward (AF) strategy is employed at the relay, with a goal of maximizing the sum secrecy rate of the two-way transmissions. This sum secrecy rate maximization (SSRM) problem is nonconvex by nature, but can be converted into the form of the difference-of-concave (DC) functions after the semidefinite relaxation (SDR). Thus, the classical DC programming naturally applies. We prove that the SDR is tight and give a specific way to recover a stationary solution of the SSRM problem from the relaxed DC problem. Moreover, to reduce the iteration complexity of DC, we proposed an inexact DC framework, which uses an approximate solution to iterate, rather than a globally optimal one. The convergence of the inexact DC to a stationary solution of the SSRM problem is also established.