Robust Activity Detection for Massive Access using Covariance-based Matching Pursuit
Robust Activity Detection for Massive Access using Covariance-based Matching Pursuit
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.
Peer reviewed
- Aalto University Finland
Speech processing, Performance evaluation, Acoustics, Noise, Robustness, Signal processing algorithms, Accuracy, Matching pursuit algorithms
Speech processing, Performance evaluation, Acoustics, Noise, Robustness, Signal processing algorithms, Accuracy, Matching pursuit algorithms
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