Reliable activity detection for massive machine to machine communication via multiple measurement vector compressed sensing

Monsees, Fabian; Bockelmann, Carsten; Dekorsy, Armin

doi:10.1109/glocomw.2014.7063573

Cited by 11 publications

(13 citation statements)

References 12 publications

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“…References CS-Multiuser detection algorithms [6,[21][22][23][24][25][26][27][28][29][30][31][32][33][34] CS-Medium access schemes [35][36][37][38][39][40][41][42][43][44] Combination of CS-MUD with other techniques [45][46][47][48][49][50] To overcome the limitations of the code domain NOMA and to more efficiently exploit the MTC traffic pattern, recently, compressive sensing based multiuser detection (CS-MUD) techniques are used for enabling the code domain NOMA. In order to accommodate large number of nodes within the available resources, non-orthogonal spreading sequences are assigned to the nodes.…”

Section: Categorymentioning

confidence: 99%

“…In [32], MMV-CS model was considered to reduce the complexity of the receiver and increase the computation speed. The activity is detected based on the covariance matrix of the received signal Y ∈ C Ns×N d which is given as [32]…”

Section: Sparsity Aware Map Based Algorithmsmentioning

confidence: 99%

“…Considering the MMV-CS model, besides the MAP based algorithm in Section 4.1.1, the authors in [32] also presented a greedy algorithm called matrix matching pursuit (MMP) for the activity detection. MMP is an extension of OMP, which selects the active users based on the maximum correlation of the spreading sequences with the sample covariance matrix, Φ Y Y , of the received matrix Y in Equation (7).…”

Section: Matrix Matching Pursuitmentioning

confidence: 99%

“…The proposed algorithm improves the activity detection with the complexity invariant to the length of the frame. However, in [32], the proposed algorithms are only for activity detection and a separate data detection has to be implemented once the support is obtained. Moreover, the performance evaluation considers only the AWGN channel and the effect of fading channels is not incorporated.…”

Section: Matrix Matching Pursuitmentioning

confidence: 99%

“…Therefore, in case of massive mMTC where the number of devices is higher, allocating low correlated sequences to each node will be a challenging task. -Complexity is independent of Frame length -Robust to asynchronous transmissions -Additional data estimation step -Simulation parameters are not realistic for mMTC [32]…”

Section: Baseline Non-orthogonal Medium Accessmentioning

confidence: 99%

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Non-Orthogonal Multiple Access With Sequence Block Compressed Sensing Multiuser Detection for 5G

Alam

Zhang

2018

IEEE Access

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One objective of the 5G communication system and beyond is to support massive machine type of communication (mMTC) to propel the fast growth of diverse Internet of Things use cases. The mMTC aims to provide connectivity to tens of billions sensor nodes. The dramatic increase of sensor devices and massive connectivity impose critical challenges for the network to handle the enormous control signaling overhead with limited radio resource. Non-Orthogonal Multiple Access (NOMA) is a new paradigm shift in the design of multiple user detection and multiple access. NOMA with compressive sensing based multiuser detection is one of the promising candidates to address the challenges of mMTC. The survey article aims at providing an overview of the current state-of-art research work in various compressive sensing based techniques that enable NOMA. We present characteristics of different algorithms and compare their pros and cons, thereby provide useful insights for researchers to make further contributions in NOMA using compressive sensing techniques.Keywords Non-orthogonal CDMA · massive connectivity · grant free medium access · compressive sensing multiuser detection IntroductionThe fifth generation (5G) wireless communication system envisions three major use cases: (i) enhanced mobile broadband (eMBB), (ii) ultra-reliable and

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Section: Categorymentioning

confidence: 99%

Section: Sparsity Aware Map Based Algorithmsmentioning

confidence: 99%

Section: Matrix Matching Pursuitmentioning

confidence: 99%

Section: Matrix Matching Pursuitmentioning

confidence: 99%

Section: Baseline Non-orthogonal Medium Accessmentioning

confidence: 99%

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Non-Orthogonal Multiple Access With Sequence Block Compressed Sensing Multiuser Detection for 5G

Alam

Zhang

2018

IEEE Access

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Machine-type communications

Sachs

Popovski

Höglund

et al. 2016

5G Mobile and Wireless Communications Technology

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Reconstruction of Finite-Alphabet Block-Sparse Signals From MAP Support Detection

et al. 2019

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This paper addresses finite-alphabet block-sparse signal recovery by considering support detection and data estimation separately. To this aim, we propose a maximum a posteriori (MAP) support detection criterion that takes into account the finite alphabet of the signal as a constraint. We then incorporate the MAP criterion in a compressed sensing detector based on a greedy algorithm for support estimation. We also propose to consider the finite-alphabet property of the signal in the bound-constrained least-squares optimization algorithm for data estimation. The MAP support detection criterion is investigated in two different contexts: independent linear modulation symbols and dependent binary continuous phase modulation (CPM) symbols. The simulations are carried out in the context of sporadic multiuser communications and show the efficiency of proposed algorithms compared to selected state-of-the-art algorithms both in terms of support detection and data estimation.

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Reliable activity detection for massive machine to machine communication via multiple measurement vector compressed sensing

Cited by 11 publications

References 12 publications

Non-Orthogonal Multiple Access With Sequence Block Compressed Sensing Multiuser Detection for 5G

Non-Orthogonal Multiple Access With Sequence Block Compressed Sensing Multiuser Detection for 5G

Machine-type communications

Reconstruction of Finite-Alphabet Block-Sparse Signals From MAP Support Detection

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