Enhanced compressed sensing based multiuser detection for machine type communication

Alam, Mehmood; Zhang, Qi

doi:10.1109/wcnc.2018.8377222

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…Access. The medium access scheme in the MCSM is enhanced in [43] by introducing spreading sequence diversity. Each user uses two spreading sequences from the sensing matrix, A, to spread their data frame d. Half of the data symbols are spread over one sequence and the other half over the other as shown in Figure 9.…”

Section: Enhanced Nonorthogonal Mediummentioning

confidence: 99%

A Survey: Nonorthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC

Alam

Zhang

2021

Wireless Communications and Mobile Computing

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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 is aimed at providing connectivity to tens of billions of 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 resources. Nonorthogonal 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 is aimed 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 providing useful insights for researchers to make further contributions in NOMA using compressive sensing techniques. Nonorthogonal CDMA massive connectivity grant free medium access compressive sensing multiuser detection

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Section: Enhanced Nonorthogonal Mediummentioning

confidence: 99%

A Survey: Nonorthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC

Alam

Zhang

2021

Wireless Communications and Mobile Computing

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“…Applying an MMV framework to MUD enables underdetermined (M < N ) applications but has generally assumed that any active user is active for the entire frame of observation (a row of X is entirely zero or nonzero) [49,50]. Recently, the potency of sensor groups with a G = 2 system was demonstrated in the MUD context [51]. Despite some similarities to MMVP with an MMV framework and discrete signals, MUD most fundamentally differs from MMVP in its utilization of the probabilistic structure of X.…”

Section: Previous Workmentioning

confidence: 99%

Extreme Compressed Sensing of Poisson Rates from Multiple Measurements

Kota,

LeJeune,

Drezek

et al. 2021

Preprint

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Compressed sensing (CS) is a signal processing technique that enables the efficient recovery of a sparse high-dimensional signal from low-dimensional measurements. In the multiple measurement vector (MMV) framework, a set of signals with the same support must be recovered from their corresponding measurements. Here, we present the first exploration of the MMV problem where signals are independently drawn from a sparse, multivariate Poisson distribution. We are primarily motivated by a suite of biosensing applications of microfluidics where analytes (such as whole cells or biomarkers) are captured in small volume partitions according to a Poisson distribution. We recover the sparse parameter vector of Poisson rates through maximum likelihood estimation with our novel Sparse Poisson Recovery (SPoRe) algorithm. SPoRe uses batch stochastic gradient ascent enabled by Monte Carlo approximations of otherwise intractable gradients. By uniquely leveraging the Poisson structure, SPoRe substantially outperforms a comprehensive set of existing and custom baseline CS algorithms. Notably, SPoRe can exhibit high performance even with one-dimensional measurements and high noise levels. This resource efficiency is not only unprecedented in the field of CS but is also particularly potent for applications in microfluidics in which the number of resolvable measurements per partition is often severely limited. We prove the identifiability property of the Poisson model under such lax conditions, analytically develop insights into system performance, and confirm these insights in simulated experiments. Our findings encourage a new approach to biosensing and are generalizable to other applications featuring spatial and temporal Poisson signals.

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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%

“…The medium access scheme in the MCSM is enhanced in [43] by introducing spreading sequence diversity. Each user uses two spreading sequences from the sensing matrix, A, to spread their data frame d. Half of the data symbols are spread over one sequence and the other half over the other as shown in Figure 8.…”

Section: Enhanced Non-orthogonal Medium Accessmentioning

confidence: 99%

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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Enhanced compressed sensing based multiuser detection for machine type communication

Cited by 14 publications

References 8 publications

A Survey: Nonorthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC

A Survey: Nonorthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC

Extreme Compressed Sensing of Poisson Rates from Multiple Measurements

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

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