Reconfigurable Intelligent Surfaces (RISs) offer a promising means of reshaping the wireless propagation environment, yet practical methods for configuring large passive arrays to achieve reliable signal equalization remain limited. Equalization is essential in wideband links to counteract multipath-induced pulse distortion that otherwise degrades symbol recovery. This work investigates RIS-assisted pulse response equalization and signal boosting using both classical adaptive filtering and model-free deep reinforcement learning (DRL). We develop a steepest descent (SD) method that exploits cascaded BS-RIS-UE channel information to configure RIS coefficients for multipath mitigation and SNR enhancement, and we show that the tradeoffs between SD and DRL primarily arise from the extensive channel estimation required for accurate equalization with passive RIS hardware. Unlike traditional adaptive filtering, which updates delayed filter coefficients after signal reception, our approach uses the RIS positioned within the cascaded channel to perform equalization without delay elements, prior to reception at the UE. In this framework, the channel is estimated before equalization, forming the basis of what we term adaptive RIS equalization (ARISE). To overcome the reliance on channel estimation required for ARISE, we explore several DRL algorithms -- DDPG, TD3, and SAC -- that optimize RIS coefficients directly from the received pulse response without explicit channel estimation. Through extensive simulations across diverse channel conditions and RIS sizes, we show that SAC achieves fast, stable convergence and equalization performance comparable to ARISE while offering significantly lower implementation complexity. These results highlight the potential of DRL as a practical and scalable solution for real-time RIS control in future wireless systems.

翻译：可重构智能表面（RIS）为重塑无线传播环境提供了前景广阔的技术手段，然而，配置大规模无源阵列以实现可靠信号均衡的实用方法仍然有限。在宽带链路中，均衡对于抵消多径效应引起的脉冲失真至关重要，否则将导致符号恢复性能下降。本研究结合经典自适应滤波与无模型深度强化学习（DRL），探索了RIS辅助的脉冲响应均衡与信号增强。我们开发了一种最速下降（SD）方法，该方法利用级联的基站-RIS-用户设备信道信息来配置RIS系数，以实现多径抑制与信噪比提升，并揭示了SD与DRL之间的权衡主要源于无源RIS硬件为实现精确均衡所需的大量信道估计。与传统自适应滤波在信号接收后更新延迟滤波器系数不同，我们的方法将RIS置于级联信道中，在用户设备接收信号之前执行无延迟元件的均衡。在此框架下，信道在均衡前被估计，构成了我们称之为自适应RIS均衡（ARISE）的基础。为克服ARISE对信道估计的依赖，我们探索了多种DRL算法——DDPG、TD3与SAC——这些算法无需显式信道估计，可直接根据接收到的脉冲响应优化RIS系数。通过在不同信道条件与RIS规模下的大量仿真实验，我们证明SAC能够实现快速稳定的收敛，其均衡性能与ARISE相当，同时具有显著更低的实现复杂度。这些结果凸显了DRL作为未来无线系统中实时RIS控制的实用化、可扩展解决方案的潜力。