Deep Learning-Based Channel Estimation and Dynamic IRS Assignment for Optimized Beamforming in IRS-Aided MC-NOMA Systems

This paper proposes a novel framework for optimizing beamforming gain in multicarrier non-orthogonal multiple access (MC-NOMA) systems that are assisted by intelligent reflecting surfaces (IRS).The framework includes channel estimation based on deep learning, dynamic IRS assignment, and beamforming optimization.First, a convolutional neural network-long short-term memory (CNN-LSTM) model was implemented to estimate the channel state information (CSI) accurately.After that, a Qlearning agent utilizes this estimated CSI to allocate IRS elements to user clusters in a dynamic manner, with the objective of optimizing the use of IRS units according to the existing channel conditions.Using the allocated IRS elements and the estimated CSI, a deep Q-network (DQN) is developed to find the best beamforming vectors that save the most power and get the optimum signal-to-interference-plus-noise ratio (SINR).When compared to random IRS assignment and traditional heuristic beamforming optimization methods, the results show that the proposed framework shows significant improvements in SINR, power efficiency, and total system capacity.Simulation results show that the integration of deep learning and reinforcement learning techniques in the IRS-assisted MC-NOMA system can significantly improve the performance, indicating that the proposed framework is a potential solution for wireless communication in the future.