Joint Estimation of Clustered User Activity and Correlated Channels with Unknown Covariance in mMTC

TL;DR

This paper proposes a novel joint user activity detection and channel estimation method for clustered user patterns in mMTC, addressing correlated channels with unknown covariance, using an ADMM-based algorithm for improved performance.

Contribution

It introduces a MAP-based formulation and an ADMM algorithm for joint estimation in clustered, correlated channels with unknown covariance matrices, advancing mMTC network capabilities.

Findings

Significant improvement in activity detection accuracy.

Enhanced channel estimation performance.

Effective handling of correlated channels with unknown covariance.

Abstract

This paper considers joint user identification and channel estimation (JUICE) in grant-free access with a \emph{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 \emph{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.