Exploiting Temporal Side Information in Massive IoT Connectivity

Research output: Journal article publicationJournal articleAcademic researchpeer-review


This paper considers the joint device activity detection and channel estimation problem in a massive Internet of Things (IoT) connectivity system, where a large number of IoT devices exist but merely a random subset of them become active for short-packet transmission in each coherence block. In particular, we propose to leverage the <italic>temporal correlation</italic> in device activity, e.g., a device active in the previous coherence block is more likely to be still active in the current coherence block, to improve the detection and estimation performance. However, it is challenging to utilize this temporal correlation as side information (SI), which relies on the knowledge about the exact statistical relation between the estimated activity pattern for the previous coherence block (which may be imperfect with unknown error) and the true activity pattern in the current coherence block. To tackle this challenge, we establish a novel <italic>SI-aided multiple measurement vector approximate message passing (MMV-AMP) framework</italic>. Specifically, thanks to the <italic>state evolution</italic> of the MMV-AMP algorithm, the correlation between the activity pattern estimated by the MMV-AMP algorithm in the previous coherence block and the real activity pattern in the current coherence block is quantified explicitly. Based on the well-defined temporal correlation, we further manage to embed this useful SI into the denoiser design under the MMV-AMP framework. Specifically, the SI-based soft-thresholding denoiser with binary thresholds and the SI-based minimum mean-squared error (MMSE) denoiser are characterized for the cases without and with the knowledge of the channel distribution, respectively. Numerical results are given to show the significant gain in device activity detection and channel estimation performance brought by our proposed SI-aided MMV-AMP framework.

Original languageEnglish
Article number9892680
Pages (from-to)1-16
Number of pages1
JournalIEEE Transactions on Wireless Communications
Publication statusAccepted/In press - 2022


  • Antenna measurements
  • approximate message passing (AMP)
  • Channel estimation
  • Coherence
  • Correlation
  • device activity detection
  • Estimation
  • grant-free random access
  • Internet of Things
  • Massive connectivity
  • Performance evaluation
  • side information (SI)
  • temporal correlation

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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