A Dimension Reduction-Based Joint Activity Detection and Channel Estimation Algorithm for Massive Access

Shao, Xiaodan; Chen, Xiaoming; Jia, Ru

doi:10.1109/tsp.2019.2961299

Cited by 80 publications

(58 citation statements)

References 51 publications

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“…As compared to (4), the extra blockwise constraint (28c) makes problem (28) difficult to solve. In this paper, we consider a heuristic method to deal with (28) by first dropping constraint (28c).…”

Section: Joint Device Activity and Data Detectionmentioning

confidence: 99%

Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO

Chen

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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This paper considers the massive random access problem in which a large number of sporadically active devices wish to communicate with a base-station (BS) equipped with a large number of antennas. Each device is preassigned a unique signature sequence, and the BS identifies the active devices in the random access by detecting which sequences are transmitted. This device activity detection problem can be formulated as a maximum likelihood estimation (MLE) problem with the sample covariance matrix of the received signal being a sufficient statistic. The aim of this paper is to characterize the parameter space in which this covariance based approach would be able to successfully recover the device activities in the massive multipleinput multiple-output (MIMO) regime. Through an analysis of the asymptotic behaviors of MLE via its associated Fisher information matrix, this paper derives a necessary and sufficient condition on the Fisher information matrix to ensure a vanishing detection error rate as the number of antennas goes to infinity, based on which a numerical phase transition analysis is obtained. This condition is also examined from a perspective of covariance matching that relates the phase transition analysis in this paper to a recently derived scaling law. Furthermore, this paper provides a characterization of the distribution of the estimation error in MLE, based on which the error probabilities in device activity detection can be accurately predicted. Finally, this paper studies a random access scheme with joint device activity and data detection and analyzes its performance in a similar way. Simulation results validate the analysis.

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Section: Joint Device Activity and Data Detectionmentioning

confidence: 99%

Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO

Chen

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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“…To enhance the detection performance, we first associate a local estimator for each AP due to the fact that different AP estimates a different device state vector. Then, to incorporate the estimates of neighboring APs, i.e., sparsity-promoting and the similarity-promoting terms [12], [34], we can modify the local estimator to associate a regularized local cost function with each AP.…”

Section: A Cooperative Massive Detection Frameworkmentioning

confidence: 99%

“…Since the proximal operator needs to be calculated at each iteration in (12) and 14, it is important to derive closedform expressions for evaluating γ t+1 b and z t b exactly. We start by calculating the gradient of local estimator f (γ b ) in (4).…”

Section: Derivation Of Algorithm Recursionsmentioning

confidence: 99%

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Cooperative Activity Detection: Sourced and Unsourced Massive Random Access Paradigms

Shao

Chen

et al. 2020

IEEE Trans. Signal Process.

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This paper investigates the issue of cooperative activity detection for grant-free random access in the sixthgeneration (6G) cell-free wireless networks with sourced and unsourced paradigms. First, we propose a cooperative framework for solving the problem of device activity detection in sourced random access. In particular, multiple access points (APs) cooperatively detect the device activity via exchanging low-dimensional intermediate information with their neighbors. This is enabled by the proposed covariance-based algorithm via exploiting both the sparsity-promoting and similarity-promoting terms of the device state vectors among neighboring APs. A decentralized approximate separating approach is introduced based on the forward-backward splitting strategy for addressing the formulated problem. Then, the proposed activity detection algorithm is adopted as a decoder of cooperative unsourced random access, where the multiple APs cooperatively detect the list of transmitted messages regardless of the identity of the transmitting devices. Finally, we provide sufficient conditions on the step sizes that ensure the convergence of the proposed algorithm in the sense of Bregman divergence. Simulation results show that the proposed algorithm is efficient for addressing both sourced and unsourced massive random access problems, while requires a shorter signature sequence and accommodates a significantly larger number of active devices with a reasonable antenna array size, compared with the state-of-art algorithms.

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“…In 2019, a structured group sparsity estimation method was developed in [17]. In [18], X. Shao et al proposed a new algorithm for active user detection, in which the received signal was decomposed into singular value decomposition (SVD), then active user detection is realized through Riemann optimization.…”

Section: Introductionmentioning

confidence: 99%

Active Users Detection Based on Orthogonal Least Squares

Zhou

Cheng

2020

IEEE Access

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The Internet of Things (IoT) is the network of numerous physical users that are able to gather and share electronic information with each other. One of the main challenges of IoT is to detect the active users. We first employ the orthogonal least squares (OLS) algorithm to detect the active users. We then prove that under some conditions on the noise vector v, OLS can successfully detect all the K active users from the linear model y = Hx + v in at most K iterations provided that H satisfies the restricted isometry property with δ K+1 < 1/ √ K + 1. Numerical tests are also performed to show the efficiency and effectiveness of the OLS algorithm. INDEX TERMS Internet of Things (IoT), Active users detection, Orthogonal least squares (OLS), Missed detection probability, False alarm detection probability.

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A Dimension Reduction-Based Joint Activity Detection and Channel Estimation Algorithm for Massive Access

Cited by 80 publications

References 51 publications

Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO

Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO

Cooperative Activity Detection: Sourced and Unsourced Massive Random Access Paradigms

Active Users Detection Based on Orthogonal Least Squares

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