{"ID":2849966,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.22557","arxiv_id":"2510.22557","title":"Large-Model AI for Near Field Beam Prediction: A CNN-GPT2 Framework for 6G XL-MIMO","abstract":"The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-field beam prediction. Unlike the conventional far-field assumption, near-field beam prediction requires codebooks that jointly sample the angular and distance domains, which leads to a dramatic increase in pilot overhead. Moreover, unlike the far-field case where the optimal beam evolution is temporally smooth, the optimal near-field beam index exhibits abrupt and nonlinear dynamics due to its joint dependence on user angle and distance, posing significant challenges for temporal modeling. To address these challenges, we propose a novel Convolutional Neural Network-Generative Pre-trained Transformer 2 (CNN-GPT2) based near-field beam prediction framework. Specifically, an uplink pilot transmission strategy is designed to enable efficient channel probing through widebeam analog precoding and frequency-varying digital precoding. The received pilot signals are preprocessed and passed through a CNN-based feature extractor, followed by a GPT-2 model that captures temporal dependencies across multiple frames and directly predicts the near-field beam index in an end-to-end manner.","short_abstract":"The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-field beam prediction. Unlike the conventional far-field assumption, near-field beam prediction requires codebooks that jointly sample the a...","url_abs":"https://arxiv.org/abs/2510.22557","url_pdf":"https://arxiv.org/pdf/2510.22557v1","authors":"[\"Wang Liu\",\"Cunhua Pan\",\"Hong Ren\",\"Wei Zhang\",\"Cheng-Xiang Wang\",\"Jiangzhou Wang\"]","published":"2025-10-26T07:24:21Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}