  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.
