📝 SummarXiv

Abstract

This paper proposes a deep learning-based framework for near-field nulling control beam focusing (NCBF) in extra-large MIMO (XL-MIMO) systems to mitigate multi-user interference (MUI). A dual-estimator architecture comprising two fully connected deep neural networks (FCDNNs) is developed to separately predict the phase and magnitude components of NCBF weights, using locations of both desired and interfering users. The models are trained on a large dataset generated via a Linearly Constrained Minimum Variance (LCMV) beamforming algorithm to accommodate diverse user configurations, including both collinear and non-collinear scenarios. Illustrative results demonstrate that the proposed DNN models achieve high prediction accuracy, with test errors of only 0.067 radians for phase estimation and 0.206 dB for magnitude estimation. Full-wave simulations incorporating realistic element radiation patterns and inter-element coupling confirm the close agreement between the beam patterns produced by the DNN-predicted and LCMV-based NCBF schemes under practical deployment conditions. An average MUI suppression of 36.7 dB is achieved, with interference mitigation exceeding 17.5 dB across all tested cases. The proposed approach enables scalable and real-time beam focusing with effective interference suppression, offering a promising solution for future near-field multi-user wireless communications.