Sparse Code Multiple Access Decoding Based on a Monte Carlo Markov Chain Method

Jienan Chen, Zhenbing Zhang, Shuaining He, Jianhao Hu, Gerald E. Sobelman

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Nonorthogonal multiple access technology has been proposed for use in 5G communications systems. In particular, the sparse code multiple access (SCMA) scheme is believed to be one of the most promising techniques among the various nonorthogonal approaches that have been investigated. In this letter, we focus on reducing the complexity of SCMA decoding and we propose a Monte Carlo Markov Chain (MCMC) based SCMA decoder. Benefiting from the linearly increasing complexity of the MCMC method, the proposed SCMA decoder has only 10% of the computational load compared to previous state-of-the-art methods when the codebook size is 64. Consequently, the MCMC SCMA decoder has great potential for use in practical system implementations.

Original languageEnglish (US)
Article number7438793
Pages (from-to)639-643
Number of pages5
JournalIEEE Signal Processing Letters
Issue number5
StatePublished - May 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 IEEE.


  • Low computational complexity
  • Monte Carlo Markov Chain (MCMC)
  • nonorthogonal multiple access
  • sparse code multiple access (SCMA)


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