RIS-Enabled Wireless Channel Equalization: Adaptive RIS Equalizer and Deep Reinforcement Learning

TL;DR

This work investigates RIS-assisted pulse response equalization and signal boosting using both classical adaptive filtering and model-free deep reinforcement learning (DRL), and develops a steepest descent method that exploits cascaded BS-RIS-UE channel information to configure RIS coefficients for multipath mitigation and SNR enhancement.

Abstract

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.

Figures (16)

• Figure 1: Illustration of the downlink RIS scenario. LoS paths are marked by solid lines, while NLoS paths are marked by dotted lines. Delayed paths and ISI components are created by reflections from obstacles.
• Figure 2: Left: conventional feedforward equalizer (FFE) at the receiver vs. right: our proposed RIS-based equalizer. The conventional equalizer processes the baseband signal after reception using $n$ delay taps, whereas the RIS-based equalizer is at the midpoint of the cascaded BS-RIS-UE link and equalizes the signal before reception by using each one of its $M$ reflection coefficients and their corresponding channels.
• Figure 3: Converged pulse responses real and imaginary parts at UE location (-20, -20), $M=100, \kappa=10,n_{\text{r}}=10$.
• Figure 4: Converged QPSK constellations at UE location (-20, -20), $M=100, \kappa=10,n_\text{r}=10$.
• Figure 5: Converged CDFs over 1000 simulations, $M=100, \kappa=10,n_{\text{r}}=10$.