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

Qiang Li, Dong-Wan Han
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引用次数: 15

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.
全双工双向中继网络的总保密率最大化
考虑一个全双工双向中继网络,在窃听者存在的情况下,两个合法节点通过一个全双工多天线中继同时发送和接收机密信息。为了保证通信的安全,在中继上采用了人工噪声辅助放大前转(AF)策略,以最大限度地提高双向传输的总保密率。该和保密率最大化(SSRM)问题本质上是非凸的,但经过半定松弛(SDR)后可转化为凹差函数(DC)形式。因此,经典的DC编程自然适用。我们证明了SDR是紧性的,并给出了从松弛DC问题中恢复SSRM问题的平稳解的具体方法。此外,为了降低数据中心的迭代复杂度,我们提出了一种非精确数据中心框架,该框架使用近似解进行迭代,而不是全局最优解。建立了非精确DC对SSRM问题平稳解的收敛性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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