Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Grant-free access is considered as a key enabler for massive machine-type communications (mMTC) it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint identification channel estimation (JUICE) main challenge. This paper addresses JUICE single-cell mMTC with single-antenna users multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging activity, we solve multi measurement vector compressed sensing (CS) framework two different cases, without knowledge of prior distribution information (CDI) at BS. First, case information, formulate an iterative reweighted $\ell _{2,1}$ -norm minimization problem. Second, when CDI known BS, exploit available from Bayesian perspective maximum a posteriori probability (MAP) For both formulations, derive efficient solutions based on alternating direction method multipliers (ADMM). The numerical experiments show that proposed achieve higher quality detection accuracy shorter pilot sequences compared existing algorithms.