A near-field wideband beamforming scheme is investigated for reconfigurable intelligent surface (RIS) assisted multiple-input multiple-output (MIMO) systems, in which a deep learning-based end-to-end (E2E) optimization framework is proposed to maximize the system spectral efficiency. To deal with the near-field double beam split effect, the base station is equipped with frequency-dependent hybrid precoding architecture by introducing sub-connected true time delay (TTD) units, while two specific RIS architectures, namely true time delay-based RIS (TTD-RIS) and virtual subarray-based RIS (SA-RIS), are exploited to realize the frequency-dependent passive beamforming at the RIS. Furthermore, the efficient E2E beamforming models without explicit channel state information are proposed, which jointly exploits the uplink channel training module and the downlink wideband beamforming module. In the proposed network architecture of the E2E models, the classical communication signal processing methods, i.e., polarized filtering and sparsity transform, are leveraged to develop a signal-guided beamforming network. Numerical results show that the proposed E2E models have superior beamforming performance and robustness to conventional beamforming benchmarks. Furthermore, the tradeoff between the beamforming gain and the hardware complexity is investigated for different frequency-dependent RIS architectures, in which the TTD-RIS can achieve better spectral efficiency than the SA-RIS while requiring additional energy consumption and hardware cost.

翻译：针对可重构智能表面（RIS）辅助的多输入多输出（MIMO）系统，提出了一种基于深度学习的端到端（E2E）优化框架，以最大化系统频谱效率，并研究了近场宽带波束赋形方案。为应对近场双波束分裂效应，基站采用频率依赖的混合预编码架构，引入子连接式真延时（TTD）单元；同时利用两种特定RIS架构——基于真延时的RIS（TTD-RIS）和基于虚拟子阵列的RIS（SA-RIS）——实现RIS处频率依赖的被动波束赋形。进一步，提出了无需显式信道状态信息的高效E2E波束赋形模型，该模型联合利用了上行信道训练模块与下行宽带波束赋形模块。在所提出的E2E模型网络架构中，借助经典通信信号处理方法（即极化滤波与稀疏变换）开发了信号引导波束赋形网络。数值结果表明，所提出的E2E模型相较于传统波束赋形基准具有更优的波束赋形性能与鲁棒性。此外，针对不同频率依赖型RIS架构，研究了波束赋形增益与硬件复杂度之间的权衡：TTD-RIS虽需额外能耗与硬件成本，但能实现优于SA-RIS的频谱效率。