Deep Learning-Based GNN-Attention Framework for Near-Field Channel Modeling and Beamforming in Sixth-Generation Wireless Systems: A Simulation Study.

Abstract

The emergence of sixth-generation (6G) networks has driven the development of Extremely Large Intelligent Metasurfaces (XL-IMS) to deliver ultra-high speeds and high spectral efficiency. Nevertheless, classical far-field channels are not applicable to near-field transmission due to spherical-wave propagation and the lack of spatial stationarity, leading to reduced beamforming performance and increased computational complexity. Therefore, the goal of this research is to develop a method for near-field channel modeling and beamforming optimization based on physics-driven deep learning. The proposed solution utilizes a physics-driven spherical wave channel model, along with graph neural networks (GNNs) and attention mechanisms, to capture local spatial relationships between users and the global network. Additionally, an algorithm for learning-based beamforming is proposed to increase the efficiency of energy focusing on near-field users. The performance of the proposed framework is evaluated using realistic propagation scenarios from the DeepMIMO dataset. The simulation results show that the proposed technique yields an achievable rate of 11.6 bps/Hz, a beamforming gain of 28 dB, a spectral efficiency of 10.5 bps/Hz, an energy efficiency of 9.8 bits/Joule, and a signal-to-interference-plus-noise ratio (SINR) of 25 dB. Compared with traditional techniques such as Zero Forcing (ZF), Minimum Mean Square Error (MMSE), Semidefinite Relaxation (SDR), and Alternating Optimization (AO), the proposed scheme improves overall communication performance by approximately 10-15%. These results reveal that integrating near-field channel modeling with GNN-based attention learning can serve as an efficient and accurate beamforming technique for future 6G wireless networks that utilize XL-IMS technology.