Iterative reweighted algorithms for joint user identification and channel estimation in spatially correlated massive MTC
Djelouat, Hamza; Leinonen, Markus; Juntti, Markku (2021-05-13)
H. Djelouat, M. Leinonen and M. Juntti, "Iterative Reweighted Algorithms for Joint User Identification and Channel Estimation in Spatially Correlated Massive MTC," ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 4805-4809, doi: 10.1109/ICASSP39728.2021.9413733
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https://urn.fi/URN:NBN:fi-fe2021080642186
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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 2,1 -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.
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