Deep Receiver Architectures for Robust MIMO Rate Splitting Multiple Access

Abstract

Machine Learning tools are becoming very powerful alternatives to improve the robustness of wireless communication systems. Signal processing procedures that tend to collapse in the presence of model mismatches can be effectively improved and made robust by incorporating the selective use of data-driven techniques. This paper explores the use of neural network (NN)-based receivers to improve the reception of a Rate Splitting Multiple Access (RSMA) system. The intention is to explore several alternatives to conventional successive interference cancellation (SIC) techniques, which are known to be ineffective in the presence of channel state information (CSI) and model errors. The focus is on NN-based architectures that do not need to be retrained at each channel realization. The main idea is to replace some of the basic operations in a conventional multi-antenna SIC receiver by their NN-based equivalents, following a hybrid Model/Data-driven based approach that preserves the main procedures in the model-based signal demodulation chain. Three different architectures are explored along with their performance and computational complexity, characterized under different degrees of model uncertainty, including imperfect channel state information and non-linear channels. We evaluate the performance of data-driven architectures in overloaded scenario to analyze its effectiveness against conventional benchmarks. The study dictates that a higher degree of robustness of transceiver can be achieved, provided the neural architecture is well-designed and fed with the right information.