Huang Ziyang, Yang Longcheng, Tan Weiqiang, Wang Han
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.
Sichuan Key Laboratory of Indoor Space Layout Optimization and Security Guarantee, Chengdu Normal University, Chengdu 611130, China.
Sensors (Basel). 2024 Jun 27;24(13):4189. doi: 10.3390/s24134189.
The exploiting of hybrid beamforming (HBF) in massive multiple-input multiple-output (MIMO) systems can enhance the system's sum rate while reducing power consumption and hardware costs. However, designing an effective hybrid beamformer is challenging, and interference between multiple users can negatively impact system performance. In this paper, we develop a scheme called Subset Optimization Algorithm-Hybrid Beamforming (SOA-HBF) that is based on the subset optimization algorithm (SOA), which effectively reduces inter-user interference by dividing the users set into subsets while optimizing the hybrid beamformer to maximize system capacity. To validate the proposed scheme, we constructed a system model that incorporates an intelligent reflecting surface (IRS) to address obstacles between the base station (BS) and the users set, enabling efficient wireless communication. Simulation results indicate that the proposed scheme outperforms the baseline by approximately 8.1% to 59.1% under identical system settings. Furthermore, the proposed scheme was applied to a classical BS-users set link without obstacles; the results show its effectiveness in both mmWave massive MIMO and IRS-assisted fully connected hybrid beamforming systems.
在大规模多输入多输出（MIMO）系统中采用混合波束成形（HBF）能够提高系统的总和速率，同时降低功耗和硬件成本。然而，设计一个有效的混合波束成形器具有挑战性，并且多用户之间的干扰会对系统性能产生负面影响。在本文中，我们开发了一种基于子集优化算法（SOA）的称为子集优化算法 - 混合波束成形（SOA - HBF）的方案，该方案通过将用户集划分为子集来有效降低用户间干扰，同时优化混合波束成形器以最大化系统容量。为了验证所提出的方案，我们构建了一个包含智能反射面（IRS）的系统模型，以解决基站（BS）与用户集之间的障碍物问题，实现高效的无线通信。仿真结果表明，在相同的系统设置下，所提出的方案比基线性能高出约8.1%至59.1%。此外，所提出的方案被应用于无障碍物的经典基站 - 用户集链路；结果表明其在毫米波大规模MIMO和IRS辅助的全连接混合波束成形系统中均有效。
