Sparse Activity Detection for Massive Connectivity

By Zhilin Chen, Foad Sohrabi, Wei Yu. IEEE Transactions on Signal Processing, 2018

361 citations recorded by the source corpus.

This paper considers the massive connectivity application in which a large number of devices communicate with a base-station (BS) in a sporadic fashion. Device activity detection and channel estimation are central problems in such a scenario. Due to the large number of potential devices, the devices need to be assigned non-orthogonal signature sequences. The main objective of this paper is to show that by using random signature sequences and by exploiting sparsity in the user activity pattern, the joint user detection and channel estimation problem can be formulated as a compressed sensing single measurement vector (SMV) or multiple measurement vector (MMV) problem depending on whether the BS has a single antenna or multiple antennas and efficiently solved using an approximate message passing (AMP) algorithm. This paper proposes an AMP algorithm design that exploits the statistics of the wireless channel and provides an analytical characterization of the probabilities of false alarm and missed detection via state evolution. We consider two cases depending on whether or not the large-scale component of the channel fading is known at the BS and design the minimum mean squared error denoiser for AMP according to the channel statistics. Simulation results demonstrate the substantial advantage of exploiting the channel statistics in AMP design; however, knowing the large-scale fading component does not appear to offer tangible benefits. For the multiple-antenna case, we employ two different AMP algorithms, namely the AMP with vector denoiser and the parallel AMP-MMV, and quantify the benefit of deploying multiple antennas.

Subfields: Computational Mechanics, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/565acdf24ebcb723c216d60a03b6e604170b110f

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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): “Under this particular distribution of x, an analytical expression of the above MMSE denoiser can be found in [14], which is in general non-linear and has complicated form.”
RadioUNet: Fast Radio Map Estimation With Convolutional Neural Networks (2019, background): “Some additional applications that rely on the knowledge of the pathloss function are fingerprint based localization [9], physical-layer security [10], power control in multi-cell massive MIMO systems [11], user pairing in MIMO-NOMA systems [12], precoding in multi-cell large scale antenna systems [1”
Massive Connectivity With Massive MIMO—Part I: Device Activity Detection and Channel Estimation (2017, methodology): “For a fixed β, the expectation term in (15) with the MMSE denoiser ηt,β (x̂t,β ) = E[Xβ |X̂ t,β = x̂t,β ] can be expressed as [18]”
Massive Access for 5G and Beyond (2020, methodology): “It is well known that the resulting sparse signal recovery problem from noisy measurements can be tackled with CS methods [63], [64].”
High-Reliability and Low-Latency Wireless Communication for Internet of Things: Challenges, Fundamentals, and Enabling Technologies (2019, methodology): “To exploit the sporadic nature of the traffic, compressed sensing is studied in [134] to formulate user”
Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO (2019, methodology): “However, the solutions in [23] and [24] require the full knowledge of the a priori distribution of the channels and the noise variance, which might not be available in practice.”