Iterative Reweighted Algorithms for Joint User Identification and Channel Estimation in Spatially Correlated Massive MTC

TL;DR

This paper introduces an iterative reweighted algorithm using ADMM for joint user identification and channel estimation in massive MTC with spatially correlated channels, improving detection and estimation accuracy.

Contribution

It formulates JUICE as an iterative reweighted $ ext{l}_{2,1}$-norm optimization and develops an ADMM-based solution that exploits channel covariance for enhanced performance.

Findings

Significant improvements in channel estimation accuracy.

Enhanced activity detection performance.

Efficient ADMM algorithm for practical implementation.

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 $\ell_{2,1}$-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.