Adaptive Position-Aware Near-Field Beam Training for Millimeter-Wave XL-MIMO

In millimeter-wave extremely large-scale multiple-input multiple-output (XL-MIMO) communications, fast and reliable beam alignment is essential for maximizing beamforming gain during data transmission. As XL-MIMO systems integrate increasingly larger antenna arrays, the electromagnetic propagation field transitions from the far-field to the near-field regime. This shift requires beam training methods that account for both angular and distance dimensions. Although recent advancements have been made in near-field beam training, existing approaches still face challenges, including high training overhead and limited robustness. The advent of advanced wireless systems has enabled the acquisition of user position information through various techniques, presenting opportunities to reduce training overhead in near-field scenarios. In this article, we propose an adaptive position-aware beam training (PABT) algorithm designed for near-field XL-MIMO systems. The PABT algorithm explicitly incorporates position errors into its design to enhance robustness. Specifically, it begins with an initialization phase that uses noisy position estimates to narrow the beam search space. Following this, a multiround iterative measurement approach is employed, where Bayes’ rule is used to update the posterior distribution of the polar-domain codebook based on the measurements obtained. This approach dynamically refines the candidate beam until a predefined probability threshold or a maximum number of iterations is reached. For the PABT proposed, we also derive a theoretical lower bound on the training overhead required to achieve a given probability threshold. This analysis provides valuable insights for selecting the key algorithm parameters in subsequent simulations. Numerical evaluations validate the proposed PABT algorithm, showing significant improvements in normalized beamforming gain and achievable spectral efficiency compared to baseline methods.