Abstract
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 language | English (US) |
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Article number | 7438793 |
Pages (from-to) | 639-643 |
Number of pages | 5 |
Journal | IEEE Signal Processing Letters |
Volume | 23 |
Issue number | 5 |
DOIs | |
State | Published - May 2016 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Low computational complexity
- Monte Carlo Markov Chain (MCMC)
- nonorthogonal multiple access
- sparse code multiple access (SCMA)