Grant-Free NOMA with Device Activity Learning Using Long Short-Term Memory

Xiaqing Miao*, Dongning Guo, Xiangming Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Non-orthogonal multiple access (NOMA) is a promising technique for future cellular networks. A major challenge in the uplink of grant-free NOMA is to identify all active devices as well as to decode their data. In the Internet of Things (IoT), the on-off activities of devices are predictable to various degrees. In this letter, a deep learning algorithm is employed to predict the device activities in the current slot by exploiting the history data. The prediction results are applied as input priors to a modified orthogonal matching pursuit (OMP) algorithm for joint device identification and data detection. Numerical simulation results demonstrate that the error rate is reduced to at least ten times as compared with conventional compressed sensing based algorithms at the same signal-to-noise ratio.

Original languageEnglish (US)
Article number9017984
Pages (from-to)981-984
Number of pages4
JournalIEEE Wireless Communications Letters
Volume9
Issue number7
DOIs
StatePublished - Jul 2020

Keywords

  • Internet of Things (IoT)
  • Non-orthogonal multiple access (NOMA)
  • deep learning
  • multiuser detection
  • orthogonal matching pursuit (OMP)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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