Neural-network-based interference cancellation for MRC and EGC receivers in large intelligent surfaces for 6G

Abstract

Large Intelligent Surfaces (LISs) have emerged as a promising technology for enhancing spectral efficiency and communication capacity in the Sixth Generation of Cellular Communications (6G). Low-complexity receiver architectures for LISs rely on Maximum Ratio Combining (MRC) and Equal Gain Combining (EGC) receivers, often complemented by iterative detection techniques for interference mitigation. In this work, we propose a novel approach where a neural network replaces iterative interference cancellation, learning to estimate the transmitted signals directly from the received data, mitigating interference without requiring iterative cancellation. Moreover, this also eliminates the need for channel matrix inversion at each frequency component, as required for Zero Forcing (ZF) and Minimum Mean Squared Error (MMSE) receivers, reducing computational complexity while still achieving a good performance improvement. The neural network parameters are optimized to balance performance and computational cost