Joint estimation of clustered user activity and correlated channels with unknown covariance in mMTC

Djelouat, Hamza; Leinonen, Markus; Juntti, Markku (2023-05-05)
 
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https://doi.org/10.1109/ICASSP49357.2023.10095199

Djelouat, Hamza
Leinonen, Markus
Juntti, Markku
Institute of Electrical and Electronics Engineers
05.05.2023

H. Djelouat, M. Leinonen and M. Juntti, "Joint Estimation of Clustered user Activity and Correlated Channels with Unknown Covariance in mMTC," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10095199.

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doi:https://doi.org/10.1109/ICASSP49357.2023.10095199
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Abstract

This paper considers joint user identification and channel estimation (JUICE) in grant-free access with a clustered user activity pattern. In particular, we address the JUICE in massive machine-type communications (mMTC) network under correlated Rayleigh fading channels with unknown channel covariance matrices. We formulate the JUICE problem as a maximum a posteriori probability (MAP) problem with properly chosen priors to incorporate the partial knowledge of the UEs’ clustered activity and the unknown covariance matrices. We derive a computationally-efficient algorithm based on alternating direction method of multipliers (ADMM) to solve the MAP problem iteratively via a sequence of closed-form updates. Numerical results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances for clustered user activity patterns.

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