In this paper, we study the codebook-based near-field beam training for intelligent reflecting surfaces (IRSs) aided wireless system. In the considered model, the near-field beam training is critical to focus signals at the location of user equipment (UE) to obtain prominent IRS array gain. However, existing codebook schemes cannot achieve low training overhead and high receiving power simultaneously. To tackle this issue, a novel two-layer codebook based beam training scheme is proposed. The layer-1 codebook is designed based on the omnidirectionality of a random-phase beam pattern, which estimates the UE distance with training overhead equivalent to that of one DFT codeword. Then, based on the estimated UE distance, the layer-2 codebook is generated to scan candidate UE locations and obtain the optimal codeword for IRS beamforming. Numerical results show that compared with benchmarks, the proposed two-layer beam training scheme achieves more accurate UE distance and angle estimation, higher data rate, and smaller training overhead.

翻译：本文研究了基于码本的智能反射表面（IRS）辅助无线系统中的近场波束训练。在所考虑的模型中，近场波束训练对于将信号聚焦在用户设备（UE）位置以获取显著的IRS阵列增益至关重要。然而，现有码本方案无法同时实现低训练开销和高接收功率。为了解决这一问题，本文提出了一种基于新型双层码本的波束训练方案。第一层码本基于随机相位波束方向图的全向性设计，能够以等效于一个DFT码字的训练开销估计UE距离。随后，基于估计的UE距离，生成第二层码本以扫描候选UE位置，并获取用于IRS波束成形的最优码字。数值结果表明，与基准方案相比，所提出的双层波束训练方案实现了更精确的UE距离和角度估计、更高的数据速率以及更低的训练开销。