Deep learning-based multi-user multi-dimensional constellation design in code domain non-orthogonal multiple access

Minsig Han, Hanchang Seo, Ameha T. Abebe, Chung G. Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered as a multi-user multi-dimensional modulation (MU-MDM) design. However, the sheer complexity of assigning multiple bits from multiple users to signal points in multi-dimension signal space, while minimizing bit-error rate (BER), had limited its practicality. Inspired by its ability to approximate complex optimization methods, this paper proposes an autoencoder (AE)-based MU-MDM design. In this regard, a novel loss function is proposed which simultaneously considers Euclidean distance between signal points and Hamming distance between bits assigned to neighboring signal points. Extensive simulation results show that the proposed AE-based design has 1.5dB gain over the state-of-the-art MU-MDM designs in both sparse and dense codebook setting. Furthermore, it is shown that the low complexity of deep learning (DL)-based receiver allows for employing dense CD-NOMA as compared to the conventional receivers which require codebooks to be sparse to reduce its implementation complexity.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728174402
DOIs
Publication statusPublished - 2020 Jun
Event2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Dublin, Ireland
Duration: 2020 Jun 72020 Jun 11

Publication series

Name2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings

Conference

Conference2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020
CountryIreland
CityDublin
Period20/6/720/6/11

Keywords

  • Autoencoder
  • Codebook design
  • Deep learning
  • Multi-dimension constellation
  • Non-orthogonal multiple access (NOMA)
  • Sparse code multiple access (SCMA)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing
  • Information Systems and Management
  • Control and Optimization

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    Han, M., Seo, H., Abebe, A. T., & Kang, C. G. (2020). Deep learning-based multi-user multi-dimensional constellation design in code domain non-orthogonal multiple access. In 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings [9145347] (2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCWorkshops49005.2020.9145347