Massive Connectivity With Massive MIMO—Part I: Device Activity Detection and Channel Estimation

By Liang Liu, Wei Yu. IEEE Transactions on Signal Processing, 2017

639 citations recorded by the source corpus.

This two-part paper considers an uplink massive device communication scenario in which a large number of devices are connected to a base station (BS), but user traffic is sporadic so that in any given coherence interval, only a subset of users is active. The objective is to quantify the cost of active user detection and channel estimation and to characterize the overall achievable rate of a grant-free two-phase access scheme in which device activity detection and channel estimation are performed jointly using pilot sequences in the first phase and data is transmitted in the second phase. In order to accommodate a large number of simultaneously transmitting devices, this paper studies an asymptotic regime where the BS is equipped with a massive number of antennas. The main contributions of Part I of this paper are as follows. First, we note that as a consequence of having a large pool of potentially active devices but limited coherence time, the pilot sequences cannot all be orthogonal. However, despite the nonorthogonality, this paper shows that in the asymptotic massive multiple-input multiple-output regime, both the missed device detection and the false alarm probabilities for activity detection can always be made to go to zero by utilizing compressed sensing techniques that exploit sparsity in the user activity pattern. Part II of this paper further characterizes the achievable rates using the proposed scheme and quantifies the cost of using nonorthogonal pilot sequences for channel estimation in achievable rates.

Subfields: Computational Mechanics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/527cf2295f96ab297020703adf429d427141cdea

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Citations in the corpus

Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things (2018, methodology): “Fix the number of antennas at the BS, M , the missed detection and false alarm probabilities from the MMSE denoiser based AMP algorithm, denoted by P t,n (M) and P FA t,n (M) (reducing to (11) and (12) when M = 1), are characterized in [6].”
Massive Access for 5G and Beyond (2020, background): “The authors in [66] further demonstrated that when the number of BTS antennas is sufficiently large, both the miss detection and the false alarm probabilities of the AMP algorithm for activity detection can approach zero asymptotically.”
Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO (2019, methodology): “To reduce the computational complexity, the authors in [23] and [24] developed an AMP-based scheme for AUD and CE for massive access in massive multiple-input multiple-output (MIMO) systems.”
Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications: Framework, Algorithms, and Analysis (2019, background): “, a large M , and massive connectivity [21], [22], i.”
Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G (2021, background): “1) Massive access: With the increasing demand for expanding cellular connectivity as well as the prosperous Internet of Things (IoT) market, the next generation cellular networks will be characterized by an excessively large number of users, a scenario also referred to as massive connectivity or mas”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “…provides a promising modeling framework to simultaneously detect the active devices and estimate their channels [75], [76], which is supported by various efﬁcient algorithms, including the approximate message passing algo-rithm [184], [185] and DNN algorithm unrolling approach [186], [97].”