Abstract:
In this paper, we provide a channel estimation-based optimization technique for maximizing the weighted sum rate (WSR) of an intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV). The model considers an IRS mounted on a UAV to predict the channel between the multi-input base station (BS) for the multi-user system using mobile IRS-aided wireless channels. We optimize the UAV position and then employ a compressive sensing-based technique that exploits the sparsity of the wireless channel, specifically, we utilize the Bayesian iterative group approximate message passing (BIG-AMP) algorithm. The strategy involves mobile IRS for efficient message passing and estimation of the channel state information between the BS and users. Using these channel estimates, we maximize the WSR for all the users by optimizing the precoding matrix at the BS and also the phase matrix at the mobile IRS.