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arXiv 2501.00546cs.ITeess.SPmath.IT
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基于非理想硬件的STAR-RIS辅助无蜂窝大规模MIMO系统性能分析与优化

Performance Analysis and Optimization of STAR-RIS-Aided Cell-Free Massive MIMO Systems Relying on Imperfect Hardware

Zeping Sui, Hien Quoc Ngo, Michail Matthaiou, Lajos Hanzo

AI总结:

本文研究非理想硬件下STAR-RIS辅助无蜂窝大规模MIMO系统,推导信道估计与频谱效率闭式表达式,并提出交替优化算法联合优化波束赋形与功率控制以提升最差用户性能。

摘要:

在空间相关衰落信道下,采用现实非理想硬件,研究了同时透射和反射可重构智能表面(STAR-RIS)辅助的无蜂窝大规模多输入多输出(CF-mMIMO)系统。具体而言,考虑了收发机失真、时变相位噪声和RIS相移误差。在考虑非理想硬件和导频污染的情况下,推导了基于线性最小均方误差(MMSE)准则的级联信道估计器。此外,基于最大比(MR)发射预编码和信道统计信息,推导了下行链路遍历频谱效率(SE)的闭式表达式,其中同时考虑了有限数量的接入点(AP)和STAR-RIS单元以及非理想硬件。进一步地,通过利用用户设备(UE)之间的遍历信干噪比(SINR),针对STAR-RIS处的无源透射和反射波束赋形(BF)以及功率控制系数的联合优化,构建了最大最小公平性问题。提出了一种交替优化(AO)算法来求解所得到的问题,其中分别提出了迭代自适应粒子群优化(APSO)和二分法来规避RIS无源BF的非凸性以及拟凹功率控制子问题。仿真结果表明,STAR-RIS辅助的CF-mMIMO系统相比其RIS辅助的对应系统获得了更高的SE。还评估了不同硬件参数的性能。此外,证明了与随机无源BF和等功率场景相关的传统方案相比,利用所提出的基于AO的算法可以显著提高最差UE的SE。

英文摘要:

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems are investigated under spatially correlated fading channels using realistic imperfect hardware. Specifically, the transceiver distortions, \textcolor{black}{time-varying phase noise, and RIS phase shift errors} are considered. Upon considering imperfect hardware and pilot contamination, we derive a linear minimum mean-square error (MMSE) criterion-based cascaded channel estimator. Moreover, a closed-form expression of the downlink ergodic spectral efficiency (SE) is derived based on maximum ratio (MR) based transmit precoding and channel statistics, where both a finite number of access points (APs) and STAR-RIS elements as well as imperfect hardware are considered. Furthermore, by exploiting the ergodic signal-to-interference-plus-noise ratios (SINRs) among user equipment (UE), a max-min fairness problem is formulated for the joint optimization of the passive transmitting and reflecting beamforming (BF) at the STAR-RIS as well as of the power control coefficients. An alternating optimization (AO) algorithm is proposed for solving the resultant problems, where iterative adaptive particle swarm optimization (APSO) and bisection methods are proposed for circumventing the non-convexity of the RIS passive BF and the quasi-concave power control sub-problems, respectively. Our simulation results illustrate that the STAR-RIS-aided CF-mMIMO system attains higher SE than its RIS-aided counterpart. The performance of different hardware parameters is also evaluated. Additionally, it is demonstrated that the SE of the worst UE can be significantly improved by exploiting the proposed AO-based algorithm compared to conventional solutions associated with random passive BF and equal-power scenarios.

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