Robust Activity Detection for Massive Access using Covariance-based Matching Pursuit

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Abstract

We propose a robust activity detection for grant free random access using greedy covariance-learning-based matching pursuit (RCL-MP) algorithm. The method incorporates a robust loss function into the Gaussian negative log-likelihood function, and uses matching pursuit framework for greedily selecting the indices of active users. This algorithm employs a flexible loss function effectively recovering sparse support under non-Gaussian noise conditions. Furthermore, we numerically demonstrate the robustness of RCL-MP across various conditions in massive access scenarios.
Original languageEnglish
Title of host publicationICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)979-8-3503-6874-1
ISBN (Print)979-8-3503-6875-8
DOIs
Publication statusPublished - 2025
MoE publication typeA4 Conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Hyderabad, India
Duration: 6 Apr 202511 Apr 2025

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
Country/TerritoryIndia
CityHyderabad
Period06/04/202511/04/2025

Keywords

  • Performance evaluation
  • Accuracy
  • Noise
  • Matching pursuit algorithms
  • Signal processing algorithms
  • Robustness
  • Acoustics
  • Speech processing

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