  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.
