Joint User Selection and Beamforming Design for Multi-IRS Aided Internet-of-Things Networks

Intelligent reflecting surface (IRS) has recently emerged as a promising technology for Internet-of-Things (IoT) networks to provide massive connectivity. In this article, we propose IoT user selection methods and beamforming designs in a multi-IRS aided IoT network. Specifically, we aim to jointly optimize the base station (BS) beamforming, IRS reflection coefficients and user selection to maximize the weighted sum rate of selected users, which is a mixed integer non-linear (MINLP) problem. To solve this MINLP problem, we design a novel algorithm by absorbing user selection implicitly into BS beamforming design, and applying a fractional programming (FP) to alternate between BS beamforming design using a Lagrangian-based subgradient method and IRS coefficients optimization using a complex circle manifold (CCM) method. However, this algorithm has high complexity, thus we further propose two low complexity and non-alternating algorithms, one using a channel correlation based metric for user selection, and the other using zero-forcing (ZF) beamforming at the BS. Numerical results show that our proposed algorithms achieve significant performance gain over benchmark schemes, and the low complexity algorithms achieve a performance comparable with the joint optimization algorithm at a fraction of the run time. These algorithms demonstrate that multiple IRSs help improve the network sum rate, IRSs with more elements tend to select IoT users more close by, and the best locations for IRS placement are near IoT device clusters.