This paper investigates the codebook based near-field beam training of Intelligent Reflecting Surface (IRS). In the considered model, near-field beam training should be performed to focus the signals at the location of user equipment (UE) to obtain the prominent IRS array gain. However, existing codebook schemes can not realize low training overhead and high receiving power, simultaneously. To tackle this issue, a novel two-layer codebook is proposed. Specifically, the layer-1 codebook is designed based on the omnidirectivity of random-phase beam pattern, which estimates the UE distance with training overhead equivalent to that of a DFT codeword. Then, based on the estimated distance of UE, the layer-2 codebook is generated to scan the candidate locations of UE, and finally obtain the optimal codeword for IRS beamforming. Numerical results show that, compared with the benchmarks, the proposed codebook scheme makes more accurate estimation of UE distances and angles, achieving higher date rate, yet with a smaller training overhead.

翻译：本文研究基于码本的智能反射面（IRS）近场波束训练问题。在所考虑模型中，需执行近场波束训练以将信号聚焦于用户设备（UE）位置，从而获得显著的IRS阵列增益。然而现有码本方案无法同时实现低训练开销与高接收功率。针对这一难题，本文提出一种新型双层码本。具体而言，第1层码本基于随机相位波束方向图的全面向性进行设计，能以与DFT码字相当的训练开销估计UE距离；随后依据估计的UE距离生成第2层码本，扫描UE候选位置，最终获得IRS波束成形的最优码字。数值结果表明，与基准方案相比，所提码本方案在UE距离与角度估计上更为精准，在保持更小训练开销的同时实现了更高数据速率。