Energy Efficiency Maximization for Aerial Intelligent Reflecting Surface-Assisted MISO Systems

Energy efficiency (EE) is a critical requirement for next-generation wireless networks, motivating the use of rate-splitting multiple access (RSMA) and intelligent reflecting surfaces (IRSs) as low-power, interference-resilient technologies. This paper studies EE maximization in a UAV-mounted IRS-assisted multi-user MISO downlink under practical hardware impairments. A communication-centric EE metric is adopted, and the optimization of RSMA precoders, common-rate allocation, IRS phase shifts, and UAV placement is formulated as a non-convex problem. To solve it efficiently, we develop an alternating optimization framework based on successive convex approximation (SCA) and rank-one relaxation. Simulation results reveal that the proposed aerial IRS-assisted RSMA design achieves substantial EE gains over NOMA and SDMA baselines and remains robust to distortion, IRS size variations, and dynamic user conditions, highlighting its suitability for energy-constrained 6G deployments.