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.

翻译：非正交多址接入（NOMA）技术已成为下一代无线网络中实现大规模连接和提升频谱效率的关键技术。本研究提出了一种新颖的双用户下行功率域NOMA框架，该框架将循环冗余校验（CRC）辅助的猜测随机加性噪声解码（GRAND）与连续干扰消除（SIC）相结合。与传统的SIC方法不同（当用户间功率差异较小时易受误差传播影响），所提方案利用GRAND以噪声为中心的策略，系统性地排序和测试候选误差模式，直至识别出正确码字。在此架构中，CRC不仅用于错误检测，还辅助解码过程，有效消除了对独立前向纠错（FEC）码的需求，降低了系统总体开销。此外，强用户通过应用基于GRAND解码的弱用户信号增强的SIC，提升了其解码性能，从而最小化误差传播并提高吞吐量。在加性高斯白噪声（AWGN）和瑞利衰落信道下，针对不同功率分配和用户距离的全面仿真结果表明，与现有先进的NOMA解码技术相比，CRC辅助的GRAND-NOMA方法显著改善了误码率（BER）性能。这些发现强调了将GRAND等通用解码方法集成到干扰受限的多用户环境中，以构建鲁棒的未来无线网络的潜力。