Near and far field

Transmitter

Algorithm

Beam (structure)

Engineering

Pure mathematics

Overhead (engineering)

Field (mathematics)

Operating system

Training (meteorology)

Telecommunications

Computer science

Linde–Buzo–Gray algorithm

Physics

Mathematics

Optics

Channel state information

Electronic engineering

Computer engineering

Artificial intelligence

Codebook

Channel (broadcasting)

Meteorology

Wireless

Base station

China Communications

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.

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