Non-Orthogonal Multiple Access (NOMA) is at the heart of a paradigm shift towards non-orthogonal communication due to its potential to scale well in massive deployments. Nevertheless, the overhead of channel estimation remains a key challenge in such scenarios. This paper introduces a data-driven, meta-learning-aided NOMA uplink model that minimizes the channel estimation overhead and does not require perfect channel knowledge. Unlike conventional deep learning successive interference cancellation (SICNet), Meta-Learning aided SIC (meta-SICNet) is able to share experience across different devices, facilitating learning for new incoming devices while reducing training overhead. Our results confirm that meta-SICNet outperforms classical SIC and conventional SICNet as it can achieve a lower symbol error rate with fewer pilots.

翻译：非正交多址接入（NOMA）因其在大规模部署中良好的可扩展性，正成为推动非正交通信范式变革的核心技术。然而，信道估计开销仍是此类场景面临的关键挑战。本文提出一种数据驱动的、基于元学习辅助的NOMA上行链路模型，该模型可最小化信道估计开销且无需完美信道知识。与传统的深度学习串行干扰消除（SICNet）不同，元学习辅助SIC（meta-SICNet）能够跨不同设备共享经验，在降低训练开销的同时促进新接入设备的学习。实验结果表明，meta-SICNet在导频数量更少的情况下仍能获得更低的符号错误率，其性能优于经典SIC和传统SICNet。