Massive Unsourced Random Access Based on Uncoupled Compressive Sensing: Another Blessing of Massive MIMO

Shyianov, Volodymyr; Bellili, Faouzi; Mezghani, Amine; Hossain, Ekram

doi:10.1109/jsac.2020.3019722

Cited by 72 publications

(60 citation statements)

References 23 publications

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“…We would like to emphasize that condition ( 6) is very pessimistic, and in practice Glauber dynamics appears to mix rapidly at substantially lower noise levels than predicted there. For example, in the setup we consider below, M = 2 14 , n = 2 11 , ν = 16, s = 128, so that the bound σ 2 0 = ν(s − 1)/4 = 508, translates into energy per transmitted bit as…”

Section: Mcmc Algorithm For Recoverymentioning

confidence: 99%

“…Over the last few years, there has been great interest in developing efficient lowcomplexity schemes for the unsourced random access channel [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. A natural approach for this setup is for all users to transmit codewords from the same codebook.…”

Section: Introductionmentioning

confidence: 99%

“…While not precisely applicable for our setup (which uses Gallager's ensemble), the following calculation could nonetheless be thought of as a crude heuristic. For example, in the setup, we consider later on in the numerical experiments, corresponding to a typical use-case for detection in unsourced random access, M = 2 14 , n = 2 11 , ν = 16, s = 128, one can solve the above equation numerically and get α * ≈ 0.993. Together with Khajehnejad et al [55], this hints that k = α * M/ ν ≈ 101 sparse, non-negative signals can be consistently recovered.…”

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confidence: 99%

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On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel

Romanov

Ordentlich

2021

Entropy

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Motivated by applications in unsourced random access, this paper develops a novel scheme for the problem of compressed sensing of binary signals. In this problem, the goal is to design a sensing matrix A and a recovery algorithm, such that the sparse binary vector x can be recovered reliably from the measurements y=Ax+σz, where z is additive white Gaussian noise. We propose to design A as a parity check matrix of a low-density parity-check code (LDPC) and to recover x from the measurements y using a Markov chain Monte Carlo algorithm, which runs relatively fast due to the sparse structure of A. The performance of our scheme is comparable to state-of-the-art schemes, which use dense sensing matrices, while enjoying the advantages of using a sparse sensing matrix.

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Section: Mcmc Algorithm For Recoverymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel

Romanov

Ordentlich

2021

Entropy

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“…Furthermore, the complexity of tree decoding exhibits an exponential increase with the number of slots. As a design alternative, Shyianov et al [12] propose an uncoupled CSbased URA scheme. It exploits the correlation between the slot-by-slot estimated channel coefficients for clustering the information chunks across different slots, thus avoiding the overhead of parity checks.…”

Section: Introductionmentioning

confidence: 99%

Unsourced Massive Random Access Scheme Exploiting Reed-Muller Sequences

Wang

Zhang

Chen

et al. 2022

IEEE Trans. Commun.

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The challenge in massive Machine Type Communication (mMTC) is to support reliable and instant access for an enormous number of machine-type devices (MTDs). In some particular applications of mMTC, the access point (AP) only has to know the messages received, but not where they source from, thus giving rise to the concept of unsourced random access (URA). In this paper, we propose a novel URA scheme exploiting the elegant properties of Reed-Muller (RM) sequences. Specifically, after dividing the message of an active user into several information chunks, RM sequences are used to carry those chunks, for exploiting the vast sequence space to improve the spectral efficiency, and their nested structure to enable reliable and efficient sequence detection. Next, we further explore a novel structural property of RM sequences for designing sparse patterns which carry part of the information and serve as the hints of coupling the information chunks of a single user. The factors affecting the performance of our slot-based RM detection are characterized. Besides, the complexity of the proposed message stitching method is analyzed and compared to the commonly used tree coding approach. Our simulation results verify the enhanced performance of the proposed URA scheme in error probability and computational complexity compared to the existing counterpart.

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“…In [10][11][12][13][14][15][16][17][18][19], schemes are presented that seek to separate user's signals and recover them using single-user coding techniques. These results are extended to multiple-input multiple-output (MIMO) scenarios in [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Decoding Algorithm for Coded Compressed Sensing with Applications to Unsourced Random Access

Amalladinne

Chamberland

et al. 2022

Sensors

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Unsourced random access (URA) has emerged as a pragmatic framework for next-generation distributed sensor networks. Within URA, concatenated coding structures are often employed to ensure that the central base station can accurately recover the set of sent codewords during a given transmission period. Many URA algorithms employ independent inner and outer decoders, which can help reduce computational complexity at the expense of a decay in performance. In this article, an enhanced decoding algorithm is presented for a concatenated coding structure consisting of a wide range of inner codes and an outer tree-based code. It is shown that this algorithmic enhancement has the potential to simultaneously improve error performance and decrease the computational complexity of the decoder. This enhanced decoding algorithm is applied to two existing URA algorithms, and the performance benefits of the algorithm are characterized. Findings are supported by numerical simulations.

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Massive Unsourced Random Access Based on Uncoupled Compressive Sensing: Another Blessing of Massive MIMO

Cited by 72 publications

References 23 publications

On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel

On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel

Unsourced Massive Random Access Scheme Exploiting Reed-Muller Sequences

An Enhanced Decoding Algorithm for Coded Compressed Sensing with Applications to Unsourced Random Access

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