Energy Constrained Sum-Rate Maximization in IRS-Assisted UAV Networks With Imperfect Channel Information

The focus of this work is maximizing the sum-rate of wireless unmanned aerial vehicle (UAV) networks with intelligent reflecting surfaces (IRS) in the presence of system practical limitations. More specifically, we consider that the phase compensation at the IRS is imperfect due to various factors such as device imperfections and channel estimation errors. Moreover, we consider that the IRS elements have limited switching frequency, which limits the possibility of being allocated to different UAVs over consecutive time slots when time-division multiple access (TDMA) is considered. To this end, we formulate an optimization problem where the objective is to maximize the network sum-rate subject to total energy and quality-of-service (QoS) constraints by optimizing the number of IRS elements and power allocated to each UAV. To solve the optimization problem, a low-complexity heuristic algorithm is proposed based on the quality of the estimated phase for each IRS element. The proposed approach is compared to benchmark techniques such as the uniform allocation process and genetic algorithm. The obtained results show that a significant sum-rate improvement of up to 45% can be gained using the proposed algorithm.