Grouped Device Detection and Channel Estimation in MTC Using a Full Bayesian Approach

This paper provides a solution for the joint activity detection and channel estimation (JADCE) problem in grant-free access with correlated device activity patterns. In particular, we consider a massive machine-type communications (mMTC) network operating in an event-triggered traffic mode. To this end, to encode the prior information about the correlated sparse activity pattern, the paper proposes a first-order Markov chain coupled with a sparsity-promoting spike and slab to capture the sparse and correlated activity pattern. Furthermore, we drive a hierarchical Bayesian parameter estimation using Markov chain Monte Carlo (MCMC) sampling to provide an efficient solution to the JADCE problem. Numerical results highlight improved activity detection accuracy and channel estimation quality performance.