Toward 6G Downlink NOMA: CRC-Aided GRAND for Noise-Resilient NOMA Decoding in Beyond-5G Networks
Non-Orthogonal Multiple Access (NOMA) technology has emerged as a promising technology to enable massive connectivity and enhanced spectral efficiency in next-generation wireless networks. In this study, we propose a novel two-user downlink power-domain NOMA framework that integrates a Cyclic Redundancy Check (CRC)-aided Guessing Random Additive Noise Decoding (GRAND) with successive interference cancellation (SIC). Unlike conventional SIC methods, which are susceptible to error propagation when there is low power disparity between users, the proposed scheme leverages GRAND's noise-centric strategy to systematically rank and test candidate error patterns until the correct codeword is identified. In this architecture, CRC is utilized not only to detect errors but also to aid the decoding process, effectively eliminating the need for separate Forward Error Correction (FEC) codes and reducing overall system overhead. Furthermore, the strong user enhances its decoding performance by applying SIC that is reinforced by GRAND-based decoding of the weaker user's signals, thereby minimizing error propagation and increasing throughput. Comprehensive simulation results over both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels, under varying power allocations and user distances, show that the CRC-aided GRAND-NOMA approach significantly improves the Bit Error Rate (BER) performance compared to state-of-the-art NOMA decoding techniques. These findings underscore the potential of integrating universal decoding methods like GRAND into interference-limited multiuser environments for robust future wireless networks.