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arXiv · 2306.05848

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

Hebatalla Issa·Mohammad Shehab·Hirley Alves
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Abstract

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.

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Hebatalla Issa, Mohammad Shehab, Hirley Alves. 2023-06-09. Meta-Learning Based Few Pilots Demodulation and Interference Cancellation For NOMA Uplink. https://arxiv.org/abs/2306.05848

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