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Angle Aware User Cooperation for Secure Massive MIMO in Rician Fading Channel

机译:在瑞典衰落渠道中安全大规模MIMO的角度意识到用户合作

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摘要

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.
机译:巨大的多输入多输出通信可以通过集中射频信号朝向合法用户来实现高级安全性。然而,如果窃听者定位本身,则该系统在瑞典衰落环境中易受攻击,使得其信道高度“相似”到合法用户的信道。为了解决这个问题,本文提出了一种角度意识的用户合作(AAUC)方案,其避免了对攻击用户的直接传输,并依赖于其他用户进行协作中继。所提出的方案仅需要窃听者的角度信息,并采用角度保密模型来表示攻击系统的平均保密率。利用这种角度模型,AAUC问题结果是非凸起的,并且提出了一种连续的凸优化算法,它会收敛到Karush-Kuhn-Tucker解决方案。此外,为大规模天线和大规模用户的情况导出了封闭式解决方案和BREGMA1一阶方法。还讨论了基于智能反射表面的扩展。仿真结果证明了所提出的连续凸优化基于AAUC方案的有效性,并且还验证了所提出的大规模优化算法的低复杂性性质。

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