Channel estimation and user association for distributed intelligent reflecting surfaces assisted MISO communications

Distributed intelligent reflecting surfaces (IRS)s deployed in wireless systems promise improved system performance, while spurring on diverse challenges such as channel estimation (CE), average signal-to-interference and noise (SINR) analysis, and IRS-user association. This is due to the passive nature of the reflecting elements and large multi-user system dimensions. In light of these challenges, we undertake the CE problem for the distributed IRSs-assisted multi-user MISO system. An optimal CE protocol requiring relatively low training overhead is developed using Bayesian techniques under the practical assumption that the base-station (BS)-IRSs channels are dominated by the line-of-sight (LoS) components. Simulation results corroborate the normalized MSE (NMSE) analysis and establish the advantage of the proposed protocol as compared to a benchmark scheme in terms of training overhead. In addition, we derive the average SINR for the distributed IRSs system with perfect channel state information (CSI) using a sub-optimal IRS reflecting configuration. After that, a successive refinement method is developed to find IRS-user association for the formulated max-min SINR problem which motivates user-fairness. Simulations validate the average SINR analysis while confirming the superiority of deploying a distributed IRSs scheme as well as an optimized IRS-user association as opposed to a centralized IRS deployment and random assignment.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat