An efficient algorithm for device detection and channel estimation in asynchronous IoT systems

Liang Liu, Ya Feng Liu

Research output: Journal article publicationConference articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

A great amount of endeavour has recently been devoted to the joint device activity detection and channel estimation problem in massive machine-type communications. This paper targets at two practical issues along this line that have not been addressed before: asynchronous transmission from uncoordinated users and efficient algorithms for real-time implementation in systems with a massive number of devices. Specifically, this paper considers a practical system where the preamble sent by each active device is delayed by some unknown number of symbols due to the lack of coordination. We manage to cast the problem of detecting the active devices and estimating their delay and channels into a group LASSO problem. Then, a block coordinate descent algorithm is proposed to solve this problem, where the closed-form solution is available when updating each block of variables with the other blocks of variables being fixed, thanks to the special structure of our interested problem. Our analysis shows that the overall complexity of the proposed algorithm is low, making it suitable for real-time application.

Original languageEnglish
Pages (from-to)4815-4819
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
Publication statusPublished - Jun 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Keywords

  • Asynchronous detection
  • Compressed sensing
  • Massive machine-type communication

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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