Intelligent Reflecting Surfaces for Wireless Networks: Deployment Architectures, Key Solutions, and Field Trials
Qingqing Wu, Guangji Chen, Qiaoyan Peng, Wen Chen, Yifei Yuan, Zhenqiao Cheng, Jianwu Dou, Zhiyong Zhao, Li Ping
Abstract
Intelligent reflecting surfaces (IRSs) have emerged as a transformative technology for wireless networks by improving coverage, capacity, and energy efficiency through intelligent manipulation of wireless propagation environments. This article provides a comprehensive study on the deployment and coordination of IRSs for wireless networks. By addressing both single- and multi-reflection IRS architectures, we examine their deployment strategies across diverse scenarios, including point-to-point, point-to-multipoint, and point/multipoint-to-area setups. For the single-reflection case, we highlight the trade-offs between passive and active IRS architectures in terms of beamforming gain, coverage extension, and spatial multiplexing. For the multi-reflection case, we discuss practical strategies to optimize IRS deployment and element allocation, balancing cooperative beamforming gains and path-loss. The article further discusses practical challenges in IRS implementation, including environmental conditions, system compatibility, and hardware limitations. Numerical results and field tests validate the effectiveness of IRS-aided wireless networks and demonstrate their capacity and coverage improvements. Lastly, promising research directions, including movable IRSs, near-field deployments, and network-level optimization, are outlined to guide future investigations.