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

Abstract

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

User Activity Detection and Channel Estimation of Spatially Correlated Channels via AMP in Massive MTC

“…Motivated by the sparse user activity pattern, several sparse recovery algorithms have been proposed to solve the JUICE such as, approximate message passing (AMP) [2]- [6], sparse Bayesian learning (SBL) [7], mixed-norm minimization [8]- [10], and deep learning [11]. In particular, AMP has been widely investigated in the context of JUICE for mMTC with grant-free access.…”

“…Recently, few works addressed the JUICE in spatially correlated MIMO channels. For instance, several mixed-norm minimization formulations using different levels of prior knowledge of the channel distribution information (CDI) have been proposed in [8]- [10], whereas, Chen et al [13] presented an orthogonal AMP algorithm to exploit both the spatial and the temporal channel correlation in mMTC systems. While these works have investigated a more practical JUICE setup, they did not provide any theoretical analysis on the user activity detection performance for the JUICE problem.…”

User Activity Detection and Channel Estimation of Spatially Correlated Channels via AMP in Massive MTC

“…Motivated by the sparse user activity pattern, several sparse recovery algorithms have been proposed to solve the JUICE such as, approximate message passing (AMP) [2]- [6], sparse Bayesian learning (SBL) [7], mixed-norm minimization [8]- [10], and deep learning [11]. In particular, AMP has been widely investigated in the context of JUICE for mMTC with grant-free access.…”

“…Recently, few works addressed the JUICE in spatially correlated MIMO channels. For instance, several mixed-norm minimization formulations using different levels of prior knowledge of the channel distribution information (CDI) have been proposed in [8]- [10], whereas, Chen et al [13] presented an orthogonal AMP algorithm to exploit both the spatial and the temporal channel correlation in mMTC systems. While these works have investigated a more practical JUICE setup, they did not provide any theoretical analysis on the user activity detection performance for the JUICE problem.…”