Deep Learning-Based Codebook Design for Code-Domain Non-Orthogonal Multiple Access: Approaching Single-User Bit-Error Rate Performance

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

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


A general form of codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered equivalent to an autoencoder (AE)-based constellation design for multi-user multidimensional modulation (MU-MDM). Due to a constrained design space for optimal constellation, e.g., fixed resource mapping and equal power allocation to all codebooks, however, existing AE architectures produce constellations with suboptimal bit-error-rate (BER) performance. Accordingly, we propose a new architecture for MU-MDM AE and underlying training methodology for joint optimization of resource mapping and a constellation design with bit-to-symbol mapping, aiming at approaching the BER performance of a single-user MDM (SU-MDM) AE model with the same spectral efficiency. The core design of the proposed AE architecture is dense resource mapping combined with the novel power allocation layer that normalizes the sum of user codebook power across the entire resources. This globalizes the domain of the constellation design by enabling flexible resource mapping and power allocation. Furthermore, it allows the AE-based training to approach a global optimal MU-MDM constellations for CD-NOMA. Extensive BER simulation results demonstrate that the proposed design outperforms the existing CD-NOMA designs while approaching the single-user BER performance achieved by the equivalent SU-MDM AE within 0.3dB over the additive white Gaussian noise channel.

Original languageEnglish
Pages (from-to)1159-1173
Number of pages15
JournalIEEE Transactions on Cognitive Communications and Networking
Issue number2
Publication statusPublished - 2022 Jun 1

Bibliographical note

Funding Information:
This work was supported in part by Samsung Research, Samsung Electronics, and in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.2020R1A2C100998412).

Publisher Copyright:
© 2015 IEEE.


  • Autoencoder (AE)
  • Codebook (CB) design
  • Deep learning (DL)
  • Multi-dimension modulation (MDM)
  • Multi-user communication
  • Non-orthogonal multiple access (NOMA)

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence


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