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：本文研究了可重构智能表面（RIS）辅助多输入多输出（MIMO）系统中的近场宽带波束成形方案，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。为应对近场双波束分裂效应，基站采用频域依赖的混合预编码架构，引入了子连接式实时延迟（TTD）单元；同时，利用两种特定的RIS架构——基于实时延迟的RIS（TTD-RIS）和基于虚拟子阵列的RIS（SA-RIS），实现RIS端的频域依赖无源波束成形。此外，本文提出了无需显式信道状态信息的高效E2E波束成形模型，该模型联合利用上行信道训练模块与下行宽带波束成形模块。在E2E模型的网络架构设计中，通过融合经典通信信号处理方法（即极化滤波与稀疏变换），构建了信号引导的波束成形网络。数值结果表明，所提E2E模型相比传统波束成形基准方法具有更优的波束成形性能与鲁棒性。进一步，本文探讨了不同频域依赖RIS架构在波束成形增益与硬件复杂度间的权衡关系，其中TTD-RIS虽需额外能耗与硬件成本，但能获得比SA-RIS更高的频谱效率。