MAP-based Active User and Data Detection for Massive Machine-Type Communications

Byeong Kook Jeong, Byonghyo Shim, Kwang Bok Lee

Research output: Contribution to journalArticle

12 Citations (Scopus)


With the advent of the Internet-of-Things (IoT), massive machine-type communications (mMTC) have become one of the most important requirements for next generation (5G) communication systems. In the mMTC scenarios, grant-free non-orthogonal multiple access (NOMA) 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 performance gain over existing algorithms.

Original languageEnglish
JournalIEEE Transactions on Vehicular Technology
Publication statusAccepted/In press - 2018 Jun 20
Externally publishedYes


  • active user detection
  • compressive sensing-based multi-user detection
  • group sparsity
  • massive machine-type communications
  • maximum a posteriori probability

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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