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

机译:MIMO两路全双工系统的鲁棒和保密率优化

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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 uncertainty model to recast our formulate RSSRM problem into 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.
机译:本文考虑了多输入多输出(MIMO)全双工(FD)双向保密系统。具体来说,两个多天线FD合法节点在存在窃听者的情况下交换他们自己的机密消息。考虑到窃听者的不完美信道状态信息(CSI),我们根据可实现的总保密率和传输功率约束的中断概率约束,制定了鲁棒的总和保密率最大化(RSSRM)问题。与其他现有的信道不确定性模型(例如范数有界和高斯分布)不同,我们利用基于矩的随机分布CSI不确定性模型将我们的RSSRM问题重新构建为基于马尔可夫不等式和鲁棒的圆锥重构的凸优化框架,即半定性编程(SDP)。另外,采用凹差(DC)近似来迭代解决这些合法节点的传输协方差矩阵。提供仿真结果以验证我们提出的FD方法。

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