# Meta-Learning Based Few Pilots Demodulation and Interference Cancellation For NOMA Uplink

arXiv: https://arxiv.org/abs/2306.05848 · HTML: https://mlsanity.com/paper/2306.05848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2023.

## Built on by (most cited first, 1 of 1)

- [Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification](https://mlsanity.com/paper/2409.12978/) (2024)

## Abstract (arXiv)

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.