Map-based active user and data detection for massive machine-Type communications

Byeong Kook Jeong, Byonghyo Shim, Kwang Bok Lee

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

With the advent of the Internet of things, massive machine-Type communications (mMTC) have become one of the most important requirements for next generation communication systems. In the mMTC scenarios, grant-free nonorthogonal multiple access on the transmission side and compressive sensing-based multi-user detection (CS-MUD) on the reception side are a promising solution because many users sporadically transmit small data packets at low rates. In this paper, we propose a novel CS-MUD algorithm for the active user and data detection in the mMTC systems. The proposed scheme consists of the maximum a posteriori probability (MAP) based active user detector (MAP-AUD) and the MAP-based data detector (MAP-DD). By exchanging the extrinsic information between MAP-AUD and MAP-DD, the proposed algorithm improves the active user detection performance and the reliability of the data detection. In addition, we extend the proposed algorithm to exploit group sparsity. By jointly processing the multiple received data with common activity, the proposed algorithm dramatically enhances the active user detection performance. We show by numerical experiments that the proposed algorithm achieves a substantial per7formance gain over existing algorithms.

Original languageEnglish
Article number8391766
Pages (from-to)8481-8494
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Volume67
Issue number9
DOIs
Publication statusPublished - 2018 Sep
Externally publishedYes

Keywords

  • Active user detection
  • Compressive sensing-based multi-user detection
  • Group sparsity
  • Massive machine-Type communications
  • Maximum a posteriori probability7

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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