Codebook design and beam training for extremely large-scale RIS: Far-field or near-field?

Abstract

Reconfigurable intelligent surface (RIS) is more likely to develop into extremely large-scale RIS (XL-RIS) to efficiently boost the system capacity for future 6G communications. Beam training is an effective way to acquire channel state information (CSI) for XL-RIS. Existing beam training schemes rely on the far-field codebook. However, due to the large aperture of XL-RIS, the scatters are more likely to be in the near-field region of XL-RIS. The far-field codebook mismatches the near-field channel model. Thus, the existing far-field beam training scheme will cause severe performance loss in the XL-RIS assisted near-field communications. To solve this problem, we propose the efficient near-field beam training schemes by designing the near-field codebook to match the near-field channel model. Specifically, we firstly design the near-field codebook by considering the near-field cascaded array steering vector of XL-RIS. Then, the optimal codeword for XL-RIS is obtained by the exhausted training procedure. To reduce the beam training overhead, we further design a hierarchical near-field codebook and propose the corresponding hierarchical near-field beam training scheme, where different levels of sub-codebooks are searched in turn with reduced codebook size. Simulation results show the proposed near-field beam training schemes outperform the existing far-field beam training scheme.