LAUSR

This letter proposes a novel dual-polarized rate-splitting multiple access (RSMA) technique for massive multiple-input multiple-output (MIMO) networks. The proposed strategy transmits common and private symbols in parallel through dynamic polarization multiplexing, and it does not require successive interference cancellation (SIC) in the reception. For assisting the design of dual-polarized MIMO-RSMA systems, we propose a deep neural network (DNN) framework for predicting the ergodic sum-rates. An efficient DNN-aided adaptive power allocation policy is also developed for maximizing the ergodic sum-rates. Simulation results validate the effectiveness of the DNNs for sum-rate prediction and power allocation and reveal that the dual-polarized MIMO-RSMA strategy can impressively outperform conventional baseline schemes.