Covariance-Based Joint Device Activity and Delay Detection in Asynchronous mMTC

Zhaorui Wang, Ya Feng Liu, Liang Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

In this letter, we study the joint device activity and delay detection problem in asynchronous massive machine-type communications (mMTC), where all active devices asynchronously transmit their preassigned preamble sequences to the base station (BS) for device identification and delay detection. We first formulate this joint detection problem as a maximum likelihood estimation problem, which depends on the received signal only through its sample covariance, and then propose efficient coordinate descent type of algorithms to solve the formulated problem. Our proposed covariance-based approach is sharply different from the existing compressed sensing (CS) approach for the same problem. Numerical results show that our proposed covariance-based approach significantly outperforms the CS approach in terms of the detection performance since our proposed approach can make better use of the BS antennas than the CS approach.

Original languageEnglish
Article number9691883
Pages (from-to)538-542
Number of pages5
JournalIEEE Signal Processing Letters
Volume29
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Asynchronous mMTC
  • Coordinate descent
  • Joint activity and delay detection
  • Random access

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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