Graph Neural Network Based Hybrid Beamforming Design in Wideband Terahertz MIMO-OFDM Systems
This addresses costly beamforming challenges in 6G wireless systems, offering a real-time alternative to traditional methods, though it is an incremental improvement in signal processing.
The paper tackles beam squint in wideband terahertz MIMO-OFDM systems by proposing a Graph Neural Network (GNN) approach for hybrid beamforming, achieving high spectral efficiency close to all-digital beamforming with reduced computational and memory burdens.
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.