[论文解读] Codebook Design and Beam Training for Extremely Large-Scale RIS: Far-Field or Near-Field?

Xiuhong Wei, Linglong Dai+3 more|arXiv (Cornell University)|Sep 21, 2021

Advanced Wireless Communication Technologies被引用 10

一句话总结

本文通过设计与近场信道模型相匹配的近场码书，提出了一种针对超大规模可重构智能表面（XL-RIS）的近场波束训练方案，克服了传统远场码书带来的性能损失。分层近场波束训练方案将开销降低了90%，同时实现了最优可实现速率的92%，在近场XL-RIS系统中显著优于远场波束训练方法。

ABSTRACT

Reconfigurable intelligent surface (RIS) can improve the capacity of the wireless communication system by providing the extra link between the base station (BS) and the user. In order to resist the "multiplicative fading" effect, RIS is more likely to develop into extremely large-scale RIS (XL-RIS) for future 6G communications. Beam training is an effective way to acquire channel state information (CSI) for the XL-RIS assisted system. Existing beam training schemes rely on the far-field codebook, which is designed based on the far-field channel model. However, due to the large aperture of XL-RIS, the user is 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 between the XL-RIS and the user. In order 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 two proposed near-field beam training schemes both perform better than the existing far-field beam training scheme. Particulary, the hierarchical near-field beam training scheme can greatly reduce the beam training overhead with acceptable performance loss.

研究动机与目标

• 为解决在近场XL-RIS系统中使用远场码书导致的性能下降问题。
• 设计一种能准确建模XL-RIS近场区域中级联阵列波束成形向量的近场码书。
• 在保持高 spectral efficiency 的前提下，降低XL-RIS系统中的波束训练开销。
• 开发一种分层波束训练方案，实现多级码字搜索，同时降低复杂度。
• 验证近场波束训练在近场XL-RIS场景下相对于现有远场波束训练方法的优越性。

提出的方法

• 基于XL-RIS的近场级联阵列波束成形向量设计近场码书，考虑用户在近场区域的位置。
• 实施穷举波束训练过程，通过在整个近场码书中搜索以识别最优码字。
• 提出一种具有多级子码书的分层近场码书结构，每一级具有逐步提高的分辨率。
• 在分层码书的每一级中采用自适应采样范围和步长，并根据用户反馈进行更新，以优化搜索过程。
• 应用步长控制参数 δ = 0.25 和初始步长 Δ¹ = 4Δ，动态减小每一级的搜索空间。
• 在每一级反馈最优码字索引 l_{k,opt}，以指导下一级的搜索，从而最小化冗余探索。

Figure 1: The XL-RIS assisted wireless communication system.

实验结果

研究问题

• RQ1 为XL-RIS近场信道模型设计的近场码书是否能在波束训练中优于传统远场码书？
• RQ2 分层近场波束训练方案如何在保持高性能的同时降低训练开销？
• RQ3 在近场XL-RIS系统中，波束训练开销与可实现速率之间的权衡关系如何？
• RQ4 与穷举搜索相比，分层搜索策略在多大程度上保持了性能？
• RQ5 近场波束训练在XL-RIS系统中的性能与完美CSI波束成形相比如何？

主要发现

• 所提出的近场波束训练方案在性能上优于现有远场波束训练方案，尤其在XL-RIS的近场区域表现更优。
• 分层近场波束训练方案将波束训练开销降低至穷举近场方案的约10%，实现15,927个训练码字，相比147,628个。
• 分层方案实现了穷举近场波束训练方案约92%的可实现速率性能。
• 分层方案中的性能损失主要源于各级之间的误差传播，但其影响在实际部署中仍可接受。
• 由于模型失配，远场波束训练方案在近场XL-RIS场景中遭受严重性能下降。
• 仿真结果证实，近场码书设计对于实现XL-RIS系统中高性能波束训练至关重要。

Figure 2: The near-field region and the far-field region [ 21 ] .