Holographic-Pattern Based Multi-User Beam Training in RHS-Aided Hybrid Near-Field and Far-Field Communications

TL;DR

This paper introduces a novel one-shot multi-user beam training scheme for large-scale RHS-assisted systems that efficiently searches for angles and distances in both near and far fields, significantly reducing overhead while maintaining high accuracy.

Contribution

It proposes a simultaneous angle and distance search method using holographic codebooks, enabling efficient multi-user beam training in large-scale RHS systems for near and far fields.

Findings

Achieves higher system throughput than traditional schemes.

Approaches the accuracy of exhaustive search with less overhead.

Effective in both near-field and far-field regions.

Abstract

Reconfigurable holographic surfaces (RHSs) have been suggested as an energy-efficient solution for extremely large-scale arrays. By controlling the amplitude of RHS elements, high-gain directional holographic patterns can be achieved. However, the complexity of acquiring real-time channel state information (CSI) for beamforming is exceedingly high, particularly in large-scale RHS-assisted communications, where users may distribute in the near-field region of RHS. This paper proposes a one-shot multi-user beam training scheme in large-scale RHS-assisted systems applicable to both near and far fields. The proposed beam training scheme comprises two phases: angle search and distance search, both conducted simultaneously for all users. For the angle search, an RHS angular codebook is designed based on holographic principles so that each codeword covers multiple angles in both near-field and far-field regions, enabling simultaneous angular search for all users. For the distance search, we construct the distance-adaptive codewords covering all candidate angles of users in a real-time way by leveraging the additivity of holographic patterns, which is different from the traditional phase array case. Simulation results demonstrate that the proposed scheme achieves higher system throughput compared to traditional beam training schemes. The beam training accuracy approaches the upper bound of exhaustive search at a significantly reduced overhead.