Coded Beam Training for RIS Assisted Wireless Communications

2024-06-22

arXiv.org

AI Summary

To address inaccurate channel estimation and degraded beam training accuracy under low signal-to-noise ratio (SNR) conditions in reconfigurable intelligent surface (RIS)-assisted 6G communications, this paper proposes a coded beam training framework. We pioneer the integration of coding principles into RIS beam training by designing constant-modulus RIS beam codewords and formulating an optimization criterion based on a relaxed Gerchberg–Saxton algorithm. Furthermore, we introduce a novel two-dimensional dimensionality-reduction encoder that simultaneously preserves beamforming quality and enhances error-correction capability. Simulation results demonstrate that the proposed method significantly improves beam training accuracy and channel alignment robustness in low-SNR regimes, offering a new paradigm for real-time, efficient RIS configuration.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Reconfigurable intelligent surface (RIS) is considered as one of the key technologies for future 6G communications. To fully unleash the performance of RIS, accurate channel state information (CSI) is crucial. Beam training is widely utilized to acquire the CSI. However, before aligning the beam correctly to establish stable connections, the signal-to-noise ratio (SNR) at UE is inevitably low, which reduces the beam training accuracy. To deal with this problem, we exploit the coded beam training framework for RIS systems, which leverages the error correction capability of channel coding to improve the beam training accuracy under low SNR. Specifically, we first extend the coded beam training framework to RIS systems by decoupling the base station-RIS channel and the RIS-user channel. For this framework, codewords that accurately steer to multiple angles is essential for fully unleashing the error correction capability. In order to realize effective codeword design in RIS systems, we then propose a new codeword design criterion, based on which we propose a relaxed Gerchberg-Saxton (GS) based codeword design scheme by considering the constant modulus constraints of RIS elements. In addition, considering the two dimensional structure of RIS, we further propose a dimension reduced encoder design scheme, which can not only guarentee a better beam shape, but also enable a stronger error correction capability. Simulation results reveal that the proposed scheme can realize effective and accurate beam training in low SNR scenarios.

Problem

• Reconfigurable Intelligent Surface (RIS)
• Signal-to-Noise Ratio (SNR)
• Channel State Information (CSI)

Innovation

• Encoded Beam Training
• Reconfigurable Intelligent Surface (RIS)
• Channel State Information (CSI) Accuracy