Data-Driven Near-Field Beam Training and Off-Grid Optimization Scheme

The advent of extremely large-scale arrays technology is the cornerstone of the sixth-generation wireless networks, promising significant advances in spectrum efficiency. However, compared with traditional far-field beam training methods, near-field beam training faces greater challenges as the spherical wavefront propagation characteristic in the near-field environments necessitates beam search in both the angle and distance dimensions. To reduce the training overhead of the two-dimensional search, we propose a data-driven on-grid beam training scheme that can simultaneously search the angle and distance domains to find the optimal codewords through real-time adaptive alignments. To further address the grid-based non-uniform sampling, we propose a data-driven off-grid adaptive optimization scheme to further improve near-field beam training accuracy with fast convergence. By establishing equivalent dynamic linearization data models, the proposed approach adaptively adjusts the angle-distance domain estimation based on real-time measurements. Numerical results show that the proposed approach can achieve enhanced beamforming performance with reduced training overhead.