Beam-Steering Aided Hierarchical Codebook for Near-Field Beam Training

• Published in: IEEE open journal of the Communications Society Vol. 6; pp. 8335 - 8350
• Main Authors: Wang, Tao, You, Changsheng, Yin, Changchuan
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alignment; Antenna arrays; Array signal processing; Beam steering; beam training; Beamforming; Discrete Fourier transforms; extremely large-scale MIMO (XL-MIMO); Far fields; Feedback amplifiers; feedback overhead; hierarchical codebook; Linear antenna arrays; Near fields; near-field communication (NFC); Radio frequency; Radiofrequency identification; Training; training overhead; Vectors
• ISSN: 2644-125X; 2644-125X
• DOI: 10.1109/OJCOMS.2025.3615224

This article explores efficient near-field beam training strategies for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. The near-field beam training is decomposed into angle alignment (AA) and distance alignment (DA), with a focus on AA due to its more significant impact on training overhead. Drawing inspiration from conventional far-field beam steering (FFBS), we introduce the novel concept of near-field beam steering (NFBS), which enables the beam to cover arbitrary angular ranges across the entire near-field and far-field distance ranges. To realize NFBS under practical hybrid beamforming structures, we formulate the problem to approximate any target near-field beam pattern, for which an efficient codeword design approach is developed. Based on the proposed NFBS technique, a binary-tree hierarchical codebook (BTHC) and a multi-mainlobe hierarchical codebook (MMHC) are proposed to achieve efficient near-field AA under single-user equipment (UE) and multi-UE scenarios, respectively. Extensive numerical simulations are conducted, which first demonstrate that the proposed NFBS technique approximates the target near-field beam pattern well under various parameter settings. Subsequently, compared to existing benchmark schemes, the proposed BTHC achieves near-optimal achievable data rates with significantly reduced training overhead in single-UE scenarios. Moreover, the proposed MMHC scheme further reduces training and feedback overhead in multi-UE scenarios.