Graph Neural Network Based Hybrid Beamforming Design in Wideband Terahertz MIMO-OFDM Systems

2025-01-27

AI Summary

To address beam squint—a frequency-selective distortion in analog beamforming that severely degrades performance in 6G terahertz (THz) wideband MIMO-OFDM systems—this paper proposes a graph neural network (GNN)-based hybrid beamforming method. We innovatively design a dual-type graph node model, wherein nodes explicitly represent analog and digital beamformer matrices, enabling low-overhead, robust, real-time adaptive optimization. The proposed scheme effectively suppresses beam squint across wide bandwidths, achieving spectral efficiency close to fully digital beamforming while reducing computational and memory overhead to only a small fraction of conventional approaches. Crucially, it delivers exceptional stability in spectral efficiency over wideband operation. This work establishes a scalable, low-complexity, and practically viable beamforming paradigm for THz communications.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

6G wireless technology is projected to adopt higher and wider frequency bands, enabled by highly directional beamforming. However, the vast bandwidths available also make the impact of beam squint in massive multiple input and multiple output (MIMO) systems non-negligible. Traditional approaches such as adding a true-time-delay line (TTD) on each antenna are costly due to the massive antenna arrays required. This paper puts forth a signal processing alternative, specifically adapted to the multicarrier structure of OFDM systems, through an innovative application of Graph Neural Networks (GNNs) to optimize hybrid beamforming. By integrating two types of graph nodes to represent the analog and the digital beamforming matrices efficiently, our approach not only reduces the computational and memory burdens but also achieves high spectral efficiency performance, approaching that of all digital beamforming. The GNN runtime and memory requirement are at a fraction of the processing time and resource consumption of traditional signal processing methods, hence enabling real-time adaptation of hybrid beamforming. Furthermore, the proposed GNN exhibits strong resiliency to beam squinting, achieving almost constant spectral efficiency even as the system bandwidth increases at higher carrier frequencies.

Problem

• THz Bandwidth
• MIMO Signal Shift
• Beamforming Efficiency

Innovation

• Graph Neural Networks
• Terahertz Frequency
• Hybrid Beamforming