Joint Placement and Beamforming Design for IRS-Enhanced Multiuser MISO Systems

The fundamental intelligent reflecting surface (IRS) deployment problem is studied for IRS-aided downlink multi-user communication system, where IRSs are arranged to be deployed in a specific area for enhancing the desired signal and suppressing interference. Specifically, we aim to maximize the minimum achievable rate over all locations in a specific area by jointly optimizing the transmit beamforming at the access point (AP) as well as the placement and reflective beamforming at the IRS. The formulated problem is non-convex and thus difficult to be solved directly. To draw essential insights, we first consider the single-user case and the optimal solution is derived in closed-form. The result shows that the optimal locations are in the connecting line between the AP and user, and the IRSs can be optimally deployed along the connecting line. Besides, a hybrid offline and online design scheme is developed for the multi-user case, where an area discretization strategy and deep neural network (DNN)-based curve fitting technique are proposed for optimizing the IRS locations in the offline manner. Then, an online iterative algorithm is presented to solve the transmit and received beamformig vectors, respectively. Numerical results show that the performance gain is increased by optimizing the IRS locations.

I. Introduction

With the worldwide development of the fifth generation (5G) wireless communication networks, research on the new/sixth-generation (6G) wireless communication systems has been embarked. The upcoming 6G wireless network is expected to satisfy the extreme communication requirements, such as seamless holographic projection for smart working [1], real-time digital twins for smart manufacturing [2], and wireless brain-computer interactions for smart living [3]. To achieve the goals, various advanced wireless communication techniques, such as Terahertz communication, network densification, integrated air-ground networking, and ultra-massive massive multiple-input multiple-output (MIMO) have been paid attention to industry and academia [4], [5]. Despite that the emerging technologies have the great potential to improve the wireless communication performance, the required large-scale antenna arrays will be costly in terms of energy consumption, hardware implementation, signal processing complexity, especially for the Terahertz (THz) frequency. Therefore, developing high-capacity but cost-effective communication technique is important for the future wireless communication systems.