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

2026-03-02

AI Summary

This work addresses the challenge of pulse distortion caused by multipath effects in broadband wireless communications, where conventional equalization techniques are difficult to implement efficiently on passive reconfigurable intelligent surfaces (RISs). To overcome this limitation, the paper proposes an Adaptive RIS Equalizer (ARISE), which uniquely positions the RIS as a pre-equalizer within the propagation path. By leveraging cascaded channel information, ARISE directly tunes the RIS phase shifts to achieve zero-delay channel equalization and signal enhancement. To circumvent the high overhead of explicit channel estimation, a deep reinforcement learning (DRL) algorithm is introduced to optimize the RIS configuration end-to-end based solely on received pulse responses. Simulation results demonstrate that the soft actor-critic (SAC) algorithm rapidly and stably converges across diverse channel conditions and RIS scales, achieving performance close to the ideal ARISE while significantly reducing complexity, thereby validating the feasibility and scalability of DRL-driven real-time RIS control.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• Reconfigurable Intelligent Surfaces
• channel equalization
• multipath distortion
• wideband communication
• signal recovery

Innovation

• Reconfigurable Intelligent Surface
• Channel Equalization
• Deep Reinforcement Learning
• Adaptive RIS Equalizer
• Model-Free Control