Finite-Alphabet Signature Design for Grant-Free NOMA using Quantized Deep Learning

Hanxiao Yu, Zesong Fei, Zhong Zheng, Neng Ye, Sen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Grant-free Non-Orthogonal Multiple Access (NOMA) techniques are able to reduce the signaling overhead and the transmission latency in multi-user communications system. However, most of the existing code-domain grant-free NOMA schemes reuse the spreading signatures designed for the grant-based scenarios. Considering the sparsity and randomness nature of user activities in the uplink transmissions, we propose a deep learning-based signature design, where the non-equal user activation probabilities are exploited to optimize the code-domain NOMA signature. In addition, the conventional grant-free NOMA signatures are not specifically designed over finite Galois field, which hinders the implementation of the encoder/decoder using practical hardware. To address these challenges, we utilize the quantized deep learning framework for the NOMA signature training, which jointly optimizes the sequence generation and the quantization. The numerical results reveal that the obtained signatures outperform the conventional ones especially when the users has unequal activation probabilities.

Original languageEnglish
Title of host publication2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728131061
DOIs
Publication statusPublished - May 2020
Event2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Seoul, Korea, Republic of
Duration: 25 May 202028 May 2020

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2020-May
ISSN (Print)1525-3511

Conference

Conference2020 IEEE Wireless Communications and Networking Conference, WCNC 2020
Country/TerritoryKorea, Republic of
CitySeoul
Period25/05/2028/05/20

Keywords

  • Grant-free NOMA
  • deep learning
  • finite-alphabet
  • signature design
  • unequal activation probability

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Yu, H., Fei, Z., Zheng, Z., Ye, N., & Wang, S. (2020). Finite-Alphabet Signature Design for Grant-Free NOMA using Quantized Deep Learning. In 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings Article 9120594 (IEEE Wireless Communications and Networking Conference, WCNC; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCNC45663.2020.9120594