This paper investigates double RIS-assisted MIMO communication systems over Rician fading channels with finite scatterers, spatial correlation, and the existence of a double-scattering link between the transceiver. First, the statistical information is driven in closed form for the aggregated channels, unveiling various influences of the system and environment on the average channel power gains. Next, we study two active and passive beamforming designs corresponding to two objectives. The first problem maximizes channel capacity by jointly optimizing the active precoding and combining matrices at the transceivers and passive beamforming at the double RISs subject to the transmitting power constraint. In order to tackle the inherently non-convex issue, we propose an efficient alternating optimization algorithm (AO) based on the alternating direction method of multipliers (ADMM). The second problem enhances communication reliability by jointly training the encoder and decoder at the transceivers and the phase shifters at the RISs. Each neural network representing a system entity in an end-to-end learning framework is proposed to minimize the symbol error rate of the detected symbols by controlling the transceiver and the RISs phase shifts. Numerical results verify our analysis and demonstrate the superior improvements of phase shift designs to boost system performance.

翻译：本文研究了在具有有限散射体、空间相关性以及收发器之间存在双散射链路的莱斯衰落信道下的双RIS辅助MIMO通信系统。首先，推导了聚合信道的统计信息闭合表达式，揭示了系统与环境中各种因素对平均信道功率增益的影响。随后，我们针对两个目标研究了两种主动与被动波束成形设计。第一个问题通过联合优化收发器处的主动预编码与合并矩阵以及双RIS处的被动波束成形，在发射功率约束下最大化信道容量。为解决该固有的非凸问题，我们提出了一种基于交替方向乘子法（ADMM）的高效交替优化算法（AO）。第二个问题通过联合训练收发器处的编码器与解码器以及RIS处的移相器来增强通信可靠性。我们提出了一个端到端学习框架，其中每个神经网络代表系统实体，通过控制收发器与RIS的移相来最小化检测符号的误符号率。数值结果验证了我们的分析，并展示了相位偏移设计对提升系统性能的显著改进效果。