LSTM-Based Trajectory and Phase-Shift Prediction for RSMA Networks Assisted by AIRS

This paper investigates rate-splitting multiple access (RSMA) networks with multiusers assisted by aerial intelligent reflecting surfaces (AIRS). To improve the sum-rate of the system, the UAV's trajectory and phase-shift vectors are optimized, in which the mobility scenarios with static and dynamic users are explored. In particular, long short-term memory (LSTM)-based frameworks for predicting the UAV's trajectory and the phase-shift of the reflecting elements of AIRS are proposed. For more insight, a third model is created by combining information from the static and dynamic scenarios. Furthermore, to improve the transmit beamforming at the BS, an algorithm based on alternating optimization (AO) under the assumptions of imperfect successive interference cancelation (SIC) is presented. Training progress and testing results are provided to demonstrate the efficiency of the proposed models. In addition, numerical simulations are presented to verify the performance gains in terms of sum-rate. The simulation results show that the UAV performs better in trajectory prediction and phase-shift when different investigated scenarios are not combined.