Fast Near-Field Beam Training for Extremely Large-Scale Array

• Published in: IEEE wireless communications letters Vol. 11; no. 12; p. 1
• Main Authors: Zhang, Yunpu, Wu, Xun, You, Changsheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Array signal processing; Arrays; beam training; Beamforming; Benchmark testing; Channel models; Communications systems; Data communication; Data transmission; Domains; Estimation; Extremely large-scale array (XL-array); Far fields; Near fields; near-field communication; Searching; Symbols; Training; Wave fronts; Wave propagation
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2022.3212344

In this letter, we study efficient near-field beam training design for the extremely large-scale array (XL-array) communication systems. Compared with the conventional far-field beam training method that searches for the best beam direction only, the near-field beam training is more challenging since it requires a beam search over both the angular and distance domains due to the spherical wavefront propagation model. To reduce the near-field beam-training overhead based on the two-dimensional exhaustive search, we propose in this letter a new two-phase beam training method that decomposes the two-dimensional search into two sequential phases. Specifically, in the first phase, the candidate angles of the user is determined by a new method based on the conventional far-field codebook and angle-domain beam sweeping. Then, a customized polar-domain codebook is employed in the second phase to find the best effective distance of the user given the shortlisted candidate angles. Numerical results show that our proposed two-phase beam training method significantly reduces the training overhead of the exhaustive search and yet achieves comparable beamforming performance for data transmission.