Iterative Reweighted Algorithms for Joint User Identification and Channel Estimation in Spatially Correlated Massive MTC
Hamza Djelouat, Markus Leinonen, Markku J. Juntti
Abstract
Joint user identification and channel estimation (JUICE) is a main challenge in grant-free massive machine-type communications (mMTC). The sparse pattern in users’ activity allows to solve the JUICE as a compressed sensing problem in a multiple measurement vector (MMV) setup. This paper addresses the JUICE under the practical spatially correlated fading channel. We formulate the JUICE as an iterative reweighted ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</inf> -norm optimization. We develop a computationally efficient alternating direction method of multipliers (ADMM) approach to solve it. In particular, by leveraging the second-order statistics of the channels, we reformulate the JUICE problem to exploit the covariance information and we derive its ADMM-based solution. The simulation results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances.
BibTeX
@inproceedings{icassp2021_iterativereweigh,
title = {Iterative Reweighted Algorithms for Joint User Identification and Channel Estimation in Spatially Correlated Massive MTC},
author = {Hamza Djelouat and Markus Leinonen and Markku J. Juntti},
booktitle = {ICASSP 2021},
year = {2021}
}