论文标题

传播RMS收发器的SWIPT网络中的稳健总和最大化

Robust Sum-Rate Maximization in Transmissive RMS Transceiver-Enabled SWIPT Networks

论文作者

Li, Zhendong, Chen, Wen, Zhang, Ziheng, Wu, Qingqing, Cao, Huanqing, Li, Jun

论文摘要

在本文中,我们提出了一种最新的下行链路通信收发器设计,用于传播重新配置的元图(RMS)启用的同时无线信息和电力传输(SWIPT)网络。具体而言,将馈电天线部署在基于透射RMS的收发器中,该收发器可用于实现波束形成。根据波长和传播距离之间的关系,建立了平面和球形波的空间传播模型。然后,在不完善的通道状态信息(CSI)的情况下,我们制定了一个可靠的系统总和率最大化问题,该问题在考虑非线性能源收集模型和ORTAGE概率标准的同时,共同优化了RMS的传输系数,传输功率分配以及功率拆分比设计。由于优化变量的耦合,整个优化问题是非凸的,不能直接解决。因此,实现了交替优化(AO)框架来分解非凸原的原始问题。详细说明,整个问题分为三个子问题以解决。对于目标函数的非转换性,连续的凸近似(SCA)用于转换它,惩罚函数方法和范围差异(DC)编程用于处理非convex约束。最后,我们交替解决三个子问题,直到整个优化问题收敛。数值结果表明,与其他基准算法相比,我们提出的算法具有收敛性和更好的性能。

In this paper, we propose a state-of-the-art downlink communication transceiver design for transmissive reconfigurable metasurface (RMS)-enabled simultaneous wireless information and power transfer (SWIPT) networks. Specifically, a feed antenna is deployed in the transmissive RMS-based transceiver, which can be used to implement beamforming. According to the relationship between wavelength and propagation distance, the spatial propagation models of plane and spherical waves are built. Then, in the case of imperfect channel state information (CSI), we formulate a robust system sum-rate maximization problem that jointly optimizes RMS transmissive coefficient, transmit power allocation, and power splitting ratio design while taking account of the non-linear energy harvesting model and outage probability criterion. Since the coupling of optimization variables, the whole optimization problem is non-convex and cannot be solved directly. Therefore, the alternating optimization (AO) framework is implemented to decompose the non-convex original problem. In detail, the whole problem is divided into three sub-problems to solve. For the non-convexity of the objective function, successive convex approximation (SCA) is used to transform it, and penalty function method and difference-of-convex (DC) programming are applied to deal with the non-convex constraints. Finally, we alternately solve the three sub-problems until the entire optimization problem converges. Numerical results show that our proposed algorithm has convergence and better performance than other benchmark algorithms.

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