Near-Field Beam Training With Sparse DFT Codebook
Cong Zhou, Chenyu Wu, Changsheng You, Jiasi Zhou, Shuo Shi
Abstract
Extremely large-scale arrays (XL-arrays) have emerged as one promising technology to improve the spectral efficiency and spatial resolution in future sixth generation (6G) wireless systems. The drastic increase in the number of antennas renders the communication users more likely to be located in the near-field region, which requires a more accurate spherical (instead of planar) wavefront propagation modeling. However, this also inevitably incurs unaffordable beam training overhead when performing a two-dimensional (2D) beam-search in both the angular and range domains. To address this issue, we first introduce in this paper a new sparse discrete Fourier transform (DFT) codebook, which exhibits the angular periodicity in the received beam pattern at the user. This thus motivates us to propose a three-phase beam training scheme. Specifically, in the first phase, we utilize the sparse DFT codebook for beam sweeping in an angular subspace and estimate candidate user angles according to the received beam pattern. Then, a central subarray is activated to scan specific candidate angles for resolving the issue of angular ambiguity for identifying the user angle. In the third phase, the polar-domain codebook is applied in the estimated angle to search the best effective user range. Finally, numerical results show that our proposed beam training scheme enabled by the sparse DFT codebook achieves 98.67% beam training overhead reduction as compared to the exhaustive-search scheme, yet without compromising rate performance in the high signal-to-ratio (SNR) regime.