发表机构
Beijing University of Posts and Telecommunications; Southern University of Science and Technology（北京邮电大学; 南方科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对多超大规模智能反射面辅助无线通信，提出基于交替优化的近场波束成形设计，联合优化基站与反射面波束，相比传统方法显著提升接收功率。
AI 中文摘要
本文研究多超大规模智能反射面（Multi-XL-IRSs）辅助的无线通信系统中高效近场波束成形设计。以往研究大多假设远场信道条件，并基于此对基站（BS）和智能反射面（IRSs）采用基于角度的波束成形。然而，这种方法在实际近场场景中可能导致性能下降。为解决这一问题，我们考虑近场信道建模，并构建一个优化问题，旨在通过联合优化基站和智能反射面的波束成形来最大化用户设备（UE）处的接收功率。鉴于在基于波束训练（BT）的通信框架下该问题难以处理，我们重新审视并重新表述该问题。随后，提出一种基于交替优化（AO）的高效算法来求解重新表述的问题。数值结果表明，在典型设置下，我们基于AO的波束成形设计相比传统基于角度的波束成形和波束聚焦，分别提供超过170%和20%的接收功率增益。此外，在远场条件下推导的功率缩放定律在近场场景中不再成立。
英文摘要
This letter investigates efficient near-field beamforming designs for wireless communication systems aided by multiple extremely large-scale intelligent reflecting surfaces (Multi-XL-IRSs). Previous studies have mostly assumed far-field channel conditions, based on which angle-based beamforming is adopted for the base station (BS) and IRSs. However, this approach may lead to degraded performance in practical near-field scenarios. To address this issue, we consider near-field channel modeling and formulate an optimization problem aimed at maximizing the received power at the user equipment (UE) by jointly optimizing the beamforming at the BS and IRSs. Given the intractability under the beam training (BT)-based communication framework, the problem is revisited and reformulated. Subsequently, an efficient algorithm based on alternating optimization (AO) is proposed to solve the reformulated problem. Numerical results demonstrate that, in typical setups, our AO-based beamforming designs provide over 170% and 20% received power gains over conventional angle-based beamforming and beam focusing, respectively. Furthermore, the power scaling law derived under far-field conditions no longer holds in near-field scenarios.
Comments5 pages, 3 figures, accepted by IEEE Communications Letters
