Meta-learning based few pilots demodulation and interference cancellation for NOMA uplink
Issa, Hebatalla; Shehab, Mohammad; Alves, Hirley (2023-07-26)
H. Issa, M. Shehab and H. Alves, "Meta-Learning Based Few Pilots Demodulation and Interference Cancellation For NOMA Uplink," 2023 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Gothenburg, Sweden, 2023, pp. 84-89, doi: 10.1109/EuCNC/6GSummit58263.2023.10188320
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https://urn.fi/URN:NBN:fi-fe20231004138754
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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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