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 for 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.

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications

“…This is evidently sub-optimal compared to its Bayesianbased counterparts. However, it can achieve reasonable results and there have been some research efforts directed towards executing its solution in parallel, e.g., [54], [55]. As alluded to earlier, such parallel implementations cannot reap the full benefits of the proposed pilot-based cluster model because they rely on the ability to decompose the objective function as opposed to the possible isolation of the clusters of the MTDs.…”

Joint Coherent and Non-Coherent Detection and Decoding Techniques for Heterogeneous Networks

“…In particular, the AMP algorithm [8], [21], [54], the ADMM based ℓ 2,1 -minimization algorithm [44], [55], EM-SBL [56] and SOMP [57] are discussed next. Moreover, since a typical 5GB cellular network is composed of a massive number of MTDs coexisting with eMBB devices, then the computational complexity of the algorithms running at the BS is of critical importance as it influences power consumption and contributes to the carbon footprint.…”

Activity Detection in Distributed Massive MIMO With Pilot-Hopping and Activity Correlation

“…type of side-information that can be exploited is correlation of the channel between the active user and the base station. This channel can be spatially correlated [10], [11] or both spatially and temporally correlated [12]. Such side-information is used to improve methods which jointly detect the active users and estimate their channels.…”

“…After solving (15) using any convex solver, each element is thresholded to detect the active users. If A in (11) is properly normalized, it has the so-called self-regularizing property [17, Condition 1]. To show this, first note that each column of A contains exactly T non-zero entries, corresponding to the pilothopping sequence for each user.…”