Deep Learning-Aided SCMA

Minhoe Kim, Nam I. Kim, Woongsup Lee, Dong Ho Cho

Research output: Contribution to journalArticlepeer-review


Sparse code multiple access (SCMA) is a promising code-based non-orthogonal multiple-access technique that can provide improved spectral efficiency and massive connectivity meeting the requirements of 5G wireless communication systems. We propose a deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder. One benefit of D-SCMA is that the construction of an efficient codebook can be achieved in an automated manner, which is generally difficult due to the non-orthogonality and multi-dimensional traits of SCMA. We use simulations to show that our proposed scheme provides a lower BER with a smaller computation time than conventional schemes.

Original languageEnglish
Pages (from-to)720-723
Number of pages4
JournalIEEE Communications Letters
Issue number4
Publication statusPublished - 2018 Apr

Bibliographical note

Funding Information:
Manuscript received December 10, 2017; revised January 5, 2018; accepted January 5, 2018. Date of publication January 11, 2018; date of current version April 7, 2018. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01057529) and supported by ‘The Cross-Ministry Giga KOREA Project’ Grant funded by the Korea Government(MSIT) (No.GK17N0100, 5G Mobile Communication System Development based on mmWave). The associate editor coordinating the review of this letter and approving it for publication was Y. Liu. (Corresponding author: Woongsup Lee.) M. Kim, N.-I Kim, and D.-H. Cho are with the School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea.

Publisher Copyright:
© 1997-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering


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