论文标题

通过智能反射表面的多源全双工双向通信

Multiuser Full-Duplex Two-Way Communications via Intelligent Reflecting Surface

论文作者

Peng, Zhangjie, Zhang, Zhenkun, Pan, Cunhua, Li, Li, Swindlehurst, A. Lee

论文摘要

低成本的被动智能反射表面(IRS)最近被认为是一种革命性的技术,能够通过仔细调整反射元素来重新配置无线传播环境。本文提议部署IRS来涵盖蜂窝多源全双工(FD)双向通信链接的死区,同时抑制用户端自我解干(SI)和共通道干扰(CI)。根据基站(BS)和所有用户交换的信息,这种方法可能会使光谱效率增加一倍。为了确保网络公平性,我们共同优化了BS的预编码矩阵和IRS的反射系数,以最大程度地提高所有用户的加权最低率(WMR),但要遵守最大传输功率和单位模式约束。我们将这个非凸的问题重新制定,并将其分解为两个子问题。然后,通过采用块坐标下降(BCD)算法,可以交替优化等效问题中的优化变量。为了进一步降低计算复杂性,我们提出了通过在替代问题中定义较小的功能来优化预编码矩阵和反射系数向量的少量最大化(MM)算法。最后,仿真结果证实了我们提出的算法的收敛性和效率,并验证了引入IRS以改善盲区覆盖范围的优势。

Low-cost passive intelligent reflecting surfaces (IRSs) have recently been envisioned as a revolutionary technology capable of reconfiguring the wireless propagation environment through carefully tuning reflection elements. This paper proposes deploying an IRS to cover the dead zone of cellular multiuser full-duplex (FD) two-way communication links while suppressing user-side self-interference (SI) and co-channel interference (CI). Based on information exchanged by the base station (BS) and all users, this approach can potentially double the spectral efficiency. To ensure network fairness, we jointly optimize the precoding matrix of the BS and the reflection coefficients of the IRS to maximize the weighted minimum rate (WMR) of all users, subject to maximum transmit power and unit-modulus constraints. We reformulate this non-convex problem and decouple it into two subproblems. Then the optimization variables in the equivalent problem are alternately optimized by adopting the block coordinate descent (BCD) algorithm. In order to further reduce the computational complexity, we propose the minorization-maximization (MM) algorithm for optimizing the precoding matrix and the reflection coefficient vector by defining minorizing functions in the surrogate problems. Finally, simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to improve coverage in blind areas.

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