Reconfigurable intelligent surfaces (RIS)-assisted massive multiple-input multiple-output (mMIMO) is a promising technology for applications in next-generation networks. However, reflecting-only RIS provides limited coverage compared to a simultaneously transmitting and reflecting RIS (STAR-RIS). Hence, in this paper, we focus on the downlink achievable rate and its optimization of a STAR-RIS-assisted mMIMO system. Contrary to previous works on STAR-RIS, we consider mMIMO, correlated fading, and multiple user equipments (UEs) at both sides of the RIS. In particular, we introduce an estimation approach of the aggregated channel with the main benefit of reduced overhead links instead of estimating the individual channels. {Next, leveraging channel hardening in mMIMO and the use-and-forget bounding technique, we obtain an achievable rate in closed-form that only depends on statistical channel state information (CSI). To optimize the amplitudes and phase shifts of the STAR-RIS, we employ a projected gradient ascent method (PGAM) that simultaneously adjusts the amplitudes and phase shifts for both energy splitting (ES) and mode switching (MS) STAR-RIS operation protocols.} By considering large-scale fading, the proposed optimization can be performed every several coherence intervals, which can significantly reduce overhead. Considering that STAR-RIS has twice the number of controllable parameters compared to conventional reflecting-only RIS, this accomplishment offers substantial practical benefits. Simulations are carried out to verify the analytical results, reveal the interplay of the achievable rate with fundamental parameters, and show the superiority of STAR-RIS regarding its achievable rate compared to its reflecting-only counterpart.

翻译：可重构智能表面（RIS）辅助的大规模多输入多输出（mMIMO）是下一代网络应用中的一项有前景技术。然而，与同时传输和反射的RIS（STAR-RIS）相比，仅反射型RIS的覆盖范围有限。因此，本文聚焦于STAR-RIS辅助mMIMO系统的下行链路可达速率及其优化。与先前关于STAR-RIS的研究不同，我们考虑了mMIMO、相关衰落以及RIS两侧的多个用户设备（UE）。特别地，我们引入了一种聚合信道的估计方法，其主要优点在于减少链路开销，而非估计独立信道。进一步，利用mMIMO中的信道硬化特性及“使用即遗忘”边界技术，我们获得了仅依赖于统计信道状态信息（CSI）的闭合形式可达速率。为优化STAR-RIS的幅度和相移，我们采用投影梯度上升法（PGAM），该方法可同时调整能量分裂（ES）和模式切换（MS）两种STAR-RIS工作协议下的幅度和相移。通过考虑大尺度衰落，所提出的优化可在每隔几个相干间隔内执行，从而显著降低开销。鉴于STAR-RIS的可控参数数量是传统仅反射型RIS的两倍，这一成果具有重要的实际优势。仿真验证了分析结果，揭示了可达速率与基本参数之间的相互作用，并展示了STAR-RIS在可达速率方面相较于仅反射型RIS的优越性。