Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising

Yuhang Li, Yang Lu, Bo Ai, Zhiguo Ding, Dusit Niyato, Arumugam Nallanathan

TL;DR

This work develops the Hybrid Message Graph Attention Network (HMGAT), a Bidirectional Encoder Representations from Transformers (BERT)-based Noise Conditional Score Network (NCSN), and presents a Denoising Score Network (DSN) framework and its instantiation, which can denoise imperfect CSI under arbitrary channel error levels, thereby facilitating robust HBF.

Abstract

Accurate Channel State Information (CSI) is critical for Hybrid Beamforming (HBF) tasks. However, obtaining high-resolution CSI remains challenging in practical wireless communication systems. To address this issue, we propose to utilize Graph Neural Networks (GNNs) and score-based generative models to enable robust HBF under imperfect CSI conditions. Firstly, we develop the Hybrid Message Graph Attention Network (HMGAT) which updates both node and edge features through node-level and edge-level message passing. Secondly, we design a Bidirectional Encoder Representations from Transformers (BERT)-based Noise Conditional Score Network (NCSN) to learn the distribution of high-resolution CSI, facilitating CSI generation and data augmentation to further improve HMGAT's performance. Finally, we present a Denoising Score Network (DSN) framework and its instantiation, termed DeBERT, which can denoise imperfect CSI under arbitrary channel error levels, thereby facilitating robust HBF. Experiments on DeepMIMO urban datasets demonstrate the proposed models' superior generalization, scalability, and robustness across various HBF tasks with perfect and imperfect CSI.

GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising

TL;DR

This work develops the Hybrid Message Graph Attention Network (HMGAT), a Bidirectional Encoder Representations from Transformers (BERT)-based Noise Conditional Score Network (NCSN), and presents a Denoising Score Network (DSN) framework and its instantiation, which can denoise imperfect CSI under arbitrary channel error levels, thereby facilitating robust HBF.

Abstract

Accurate Channel State Information (CSI) is critical for Hybrid Beamforming (HBF) tasks. However, obtaining high-resolution CSI remains challenging in practical wireless communication systems. To address this issue, we propose to utilize Graph Neural Networks (GNNs) and score-based generative models to enable robust HBF under imperfect CSI conditions. Firstly, we develop the Hybrid Message Graph Attention Network (HMGAT) which updates both node and edge features through node-level and edge-level message passing. Secondly, we design a Bidirectional Encoder Representations from Transformers (BERT)-based Noise Conditional Score Network (NCSN) to learn the distribution of high-resolution CSI, facilitating CSI generation and data augmentation to further improve HMGAT's performance. Finally, we present a Denoising Score Network (DSN) framework and its instantiation, termed DeBERT, which can denoise imperfect CSI under arbitrary channel error levels, thereby facilitating robust HBF. Experiments on DeepMIMO urban datasets demonstrate the proposed models' superior generalization, scalability, and robustness across various HBF tasks with perfect and imperfect CSI.

Paper Structure

This paper contains 52 sections, 48 equations, 9 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. GNNs for Beamforming
  4. Node-level and Edge-level Message Passing Mechanisms
  5. Robustness-Enhancing Methods in DL-Based Communication Models
  6. Generative Models for Wireless Communications
  7. Problem Definition
  8. System Model and Sum-Rate HBF Design
  9. Graph Representation and Graph Optimization
  10. Imperfect CSI Setting
  11. Hybrid Beamforming Design via Hybrid Message Graph Attention Network
  12. Hybrid Message Graph Attention Layer
  13. Node-Level Message Passing Mechanism
  14. Edge-Level Message Passing Mechanism
  15. Feature Decoding Block
  16. ...and 37 more sections

Figures (9)

  • Figure 1: Illustration of graph representation and optimization for a $3$-user case.Left: CSI-based directed complete graph representation of the MU-MISO system. Right: graph-based HBF optimization with node-level analog/power control and edge-level digital precoding.
  • Figure 2: Illustration of the connections of the three proposed models.
  • Figure 3: Illustration of Node-level and Edge-level Message Passing Mechanisms of HMGAL.
  • Figure 4: Illustration of the overall architecture of the BERT-based NCSN.
  • Figure 5: Overall architecture of the DeBERT.
  • ...and 4 more figures