In this paper, we consider and study a cell-free massive MIMO (CF-mMIMO) system aided with reconfigurable intelligent surfaces (RISs), where a large number of access points (APs) cooperate to serve a smaller number of users with the help of RIS technology. We consider imperfect channel state information (CSI), where each AP uses the local channel estimates obtained from the uplink pilots and applies conjugate beamforming for downlink data transmission. Additionally, we consider random beamforming at the RIS during both training and data transmission phases. This allows us to eliminate the need of estimating each RIS assisted link, which has been proven to be a challenging task in literature. We then derive a closed-form expression for the achievable rate and use it to evaluate the system's performance supported with numerical results. We show that the RIS provided array gain improves the system's coverage, and provides nearly a 2-fold increase in the minimum rate and a 1.5-fold increase in the per-user throughput. We also use the results to provide preliminary insights on the number of RISs that need to be used to replace an AP, while achieving similar performance as a typical CF-mMIMO system with dense AP deployment.
翻译:在本文中,我们考虑并研究一个无细胞的大型MIMO(CF-MMIMO)系统,该系统有可重新配置的智能表面(RIS)辅助,大量接入点在RIS技术的帮助下合作为较少的用户提供服务;我们考虑频道状态信息不完善,每个AP都使用从上行试点获得的本地频道估计数据,对下行传输数据进行同步波束分析;此外,我们考虑在培训和数据传输阶段随机对RIS进行组合;这使我们能够消除估计每个RIS辅助连接的必要性,这已证明是文献中一项具有挑战性的任务;我们然后为可实现的速率形成一种封闭式表达方式,用它来评价系统绩效,并以数字结果加以支持;我们表明RIS提供的阵列增益改进了系统的覆盖范围,并提供了最低速率的近2倍增长和人均用户吞量的1.5倍增长。我们还利用结果,初步了解了需要部署的RIS数目,而这种部署是典型的文献中的一项任务。