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Coded Beam Training for RIS Assisted Wireless Communications

Yuhao Chen, Linglong Dai

TL;DR

The paper addresses beam training in RIS-assisted wireless systems under low SNR by introducing a coded beam training framework that uses channel coding to correct training errors. It develops a RIS-specific codeword design through a relaxed Gerchberg-Saxton approach and a dimension-reduced encoder to accommodate the 2D RIS structure under constant modulus constraints, enabling robust angle estimation at both the BS and RIS sides. Overhead analysis shows comparable increases over hierarchical schemes, while simulations at $f_c=28$ GHz and $N_t=64$, $N_r=256$ demonstrate improved achievable rate and success probability in challenging SNR conditions, with the encoder offering enhanced error correction. The framework promises scalable, environment-agnostic beam training for RIS systems and can extend to active RIS and mobility-enhanced scenarios, potentially benefiting large-scale mmWave/6G deployments.

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.

Coded Beam Training for RIS Assisted Wireless Communications

TL;DR

The paper addresses beam training in RIS-assisted wireless systems under low SNR by introducing a coded beam training framework that uses channel coding to correct training errors. It develops a RIS-specific codeword design through a relaxed Gerchberg-Saxton approach and a dimension-reduced encoder to accommodate the 2D RIS structure under constant modulus constraints, enabling robust angle estimation at both the BS and RIS sides. Overhead analysis shows comparable increases over hierarchical schemes, while simulations at GHz and , demonstrate improved achievable rate and success probability in challenging SNR conditions, with the encoder offering enhanced error correction. The framework promises scalable, environment-agnostic beam training for RIS systems and can extend to active RIS and mobility-enhanced scenarios, potentially benefiting large-scale mmWave/6G deployments.

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.
Paper Structure (18 sections, 32 equations, 10 figures, 2 tables, 1 algorithm)

This paper contains 18 sections, 32 equations, 10 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Prior Works
  3. Our Contributions
  4. Organization and Notation
  5. System Model and Background
  6. System Model
  7. Traditional Beam Training Frameworks
  8. Exhaustive Beam Training
  9. Hierarchical Beam Training
  10. Coded Beam Training Framework in RIS Systems
  11. Codeword Design for the Proposed Coded Beam Training Framework
  12. Proposed Relaxed Gerchberg-Saxton-based Codeword Design Scheme
  13. Proposed Dimension Reduced Encoder Design Scheme
  14. The Decoding Procedure
  15. Beam Training Overhead Analysis
  16. ...and 3 more sections

Figures (10)

  • Figure 1: Traditional beam training frameworks. (a) Exhaustive beam training; (b) Hierarchical beam training.
  • Figure 2: The designed beam shape when applying (a) GS-based codeword design scheme in lu2023hierarchical; (b) proposed relaxed GS-based codeword design scheme.
  • Figure 3: The orthogonality of $\mathbf{A}$ when the size of RIS is (a) $64\times1$; (b) $8\times8$.
  • Figure 4: Angular coverage range corresponding to the ideal beam pattern $\mathcal{V}_r$.
  • Figure 5: Angular coverage range corresponding to the proposed encoder.
  • ...and 5 more figures

Theorems & Definitions (2)

  • proof
  • proof