Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach

Şenel, Kamil; Larsson, Erik G.

doi:10.1109/tcomm.2018.2866559

Cited by 284 publications

(301 citation statements)

References 33 publications

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“…Recently a number of non-orthogonal sequence based random access schemes for mMTC have been proposed, e.g., the two-phase grant-free random access [5], the grant-free random access with data embedding [13] or data spreading [19]. The non-orthogonal sequences can be used as signatures for active device detection, e.g., [8], [11], [20], as codewords for data transmission, e.g., [12], [21], [22], or as both, e.g., [1], [13]. By detecting which sequences are transmitted, the BS acquires the identification of the active devices, or/and the data bits.…”

Section: A Related Workmentioning

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: A Related Workmentioning

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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“…Herein, we relax the rank of inactive device state matrices to the maximum number of paths. Since inactive device state matrices infinitely approach to zero matrices, even though we solve a relaxed problem, the rank relaxation would not change the solution to the original problem (5). Moreover, due to nonconvex of l 0 norm, it is quite natural to relax l 0 norm with l 1 norm.…”

Section: B Multi-rank Aware Pursuitmentioning

confidence: 99%

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

Shao

Chen

Jia

2020

IEEE Trans. Signal Process.

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Millimeter-wave/Terahertz (mmW/THz) communications have shown great potential for wideband massive access in next-generation cellular internet of things (IoT) networks. To decrease the length of pilot sequences and the computational complexity in wideband massive access, this paper proposes a novel joint activity detection and channel estimation (JADCE) algorithm. Specifically, after formulating JADCE as a problem of recovering a simultaneously sparse-group and low rank matrix according to the characteristics of mmW/THz channel, we prove that jointly imposing l0 norm and low rank on such a matrix can achieve a robust recovery under sufficient conditions, and verify that the number of measurements derived for the mmW/THz wideband massive access system is significantly smaller than currently known measurements bound derived for the conventional simultaneously sparse and low-rank recovery. Furthermore, we propose a multi-rank aware method by exploiting the quotient geometry of product of complex rank-Lmax matrices with the maximum number of scattering clusters Lmax. Theoretical analysis and simulation results confirm the superiority of the proposed algorithm in terms of computational complexity, detection error rate, and channel estimation accuracy.

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“…Thus, as in [2], we focus on the asymptotic case. Specifically, in Section III and Section IV, we consider the asymptotic analysis and optimization of the SSTR at large M, N and L and high SNR under a simple power control policy, i.e., statistical channel inversion, which can reduce the channel gain differences between users, and is especially beneficial to users with relatively weaker channel gains [5].…”

Section: Performance Metricmentioning

confidence: 99%

“…where (a) is due to (5) and the statistical channel inversion, (b) is due to the independence between α, and H, (c) is due that Pr[α n =1|H, K=k, α n =1] and Pr[ŝ W n =s W n |H, K=k, α n = α n =1] become approximately independent at large M [1], and…”

Section: Analysis Of Sstrmentioning

confidence: 99%

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Analysis and Optimization of Successful Symbol Transmission Rate for Grant-free Massive Access With Massive MIMO

et al. 2019

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Grant-free massive access is an important technique for supporting massive machine-type communications (mMTC) for Internet-of-Things (IoT). Two important features in grantfree massive access are low-complexity devices and short-packet data transmission, making the traditional performance metric, achievable rate, unsuitable in this case. In this letter, we investigate grant-free massive access in a massive multiple-input multiple-output (MIMO) system. We consider random access control, and adopt approximate message passing (AMP) for user activity detection and channel estimation in the pilot transmission phase and small phase-shift-keying (PSK) modulation in the data transmission phase. We propose a more reasonable performance metric, namely successful symbol transmission rate (SSTR), for grant-free massive access. We obtain closed-form approximate expressions for the asymptotic SSTR in the cases of maximal ratio combining (MRC) and zero forcing (ZF) beamforming at the base station (BS), respectively. We also maximize the asymptotic SSTR with respect to the access parameter and pilot length.

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Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach

Cited by 284 publications

References 33 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

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

Analysis and Optimization of Successful Symbol Transmission Rate for Grant-free Massive Access With Massive MIMO

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