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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> 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.
Highlights
Massive machine-type communications aim to provide wireless connectivity to billions of low-cost energyconstrained internet of things (IoT) devices [1]. mMTC promote three main features
We consider a single-cell of a radius of 50 m, where the base station (BS) is surrounded by N = 200 uniformly distributed user equipments (UEs), out of which K = 10 UEs are active at each coherence interval
We presented two joint user identification and channel estimation (JUICE) formulations depending on the availability of channel distribution information (CDI)
Summary
Massive machine-type communications (mMTC) aim to provide wireless connectivity to billions of low-cost energyconstrained internet of things (IoT) devices [1]. mMTC promote three main features. Energy-efficient communication protocols to ensure a long lifespan for the IoT devices, here referred to as user equipments (UEs). The main advantage of grant-free access compared to conventional random access is the reduced signalling overhead and the improved energy-efficiency of the UEs. a paramount challenge in grant-free access is to identify the set of active UEs and to estimate their channel state information for coherent data detection. A paramount challenge in grant-free access is to identify the set of active UEs and to estimate their channel state information for coherent data detection We refer to this problem as joint user identification and channel estimation (JUICE). The sparse user activity pattern induced by the sporadic transmissions in mMTC motivates the formulation of the JUICE as a compressed sensing (CS) [4]–[6] problem.
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