Massive multiple-input multiple-output communications can achieve high-level security by concentrating radio frequency signals towards the legitimate users. However, this system is vulnerable in a Rician fading environment if the eavesdropper positions itself such that its channel is highly “similar” to the channel of a legitimate user. To address this problem, this paper proposes an angle aware user cooperation (AAUC) scheme, which avoids direct transmission to the attacked user and relies on other users for cooperative relaying. The proposed scheme only requires the eavesdropper’s angle information, and adopts an angular secrecy model to represent the average secrecy rate of the attacked system. With this angular model, the AAUC problem turns out to be nonconvex, and a successive convex optimization algorithm, which converges to a Karush-Kuhn-Tucker solution, is proposed. Furthermore, a closed-form solution and a Bregman first-order method are derived for the cases of large-scale antennas and large-scale users, respectively. Extension to the intelligent reflecting surfaces based scheme is also discussed. Simulation results demonstrate the effectiveness of the proposed successive convex optimization based AAUC scheme, and also validate the low-complexity nature of the proposed large-scale optimization algorithms.

中文翻译：

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用于 Rician 衰落信道中安全大规模 MIMO 的角度感知用户合作

大规模多输入多输出通信可以通过将射频信号集中到合法用户来实现高级别安全性。但是，如果窃听者将自身定位为使其信道与合法用户的信道高度“相似”，则该系统在 Rician 衰落环境中很容易受到攻击。针对这一问题，本文提出一种角度感知用户合作（AAUC）方案，避免直接传输给被攻击用户，依赖其他用户进行合作中继。该方案只需要窃听者的角度信息，并采用角度保密模型来表示被攻击系统的平均保密率。有了这个角度模型，AAUC 问题就变成了非凸问题，一个连续的凸优化算法，提出了收敛到 Karush-Kuhn-Tucker 解的方案。此外，分别针对大规模天线和大规模用户的情况导出了封闭形式的解决方案和Bregman一阶方法。还讨论了对基于智能反射面的方案的扩展。仿真结果证明了所提出的基于连续凸优化的 AAUC 方案的有效性，也验证了所提出的大规模优化算法的低复杂性。