Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey

Wimalajeewa, Thakshila; Varshney, Pramod K.

doi:10.48550/arxiv.1709.10401

Cited by 6 publications

(14 citation statements)

References 216 publications

(469 reference statements)

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“…Moreover, it should be also considered that distances between them are compacted, due to the compression of transmitted data signals. Therefore, a factor of M /N 6 [8], [11] is weighted in the average SNR from (21) to account for that. This factor is henceforward referred as the compression rate, given its Fig.…”

Section: B Detection Performance 1) Validation Of Numerical Resultsmentioning

confidence: 99%

“…. , C}, composes a set of classes and that classes occurrences are equiprobable, the MLD decides in favor of the index î that satisfies [8], [11]…”

Section: A Maximum Likelihood Detectormentioning

confidence: 99%

“…However, the process of reconstructing signals in CS is costly and entails, for instance, solving a convex optimization problem. This motivated the search for applications where compressed signals are processed directly in the compressed domain [8], avoiding the need of signal reconstruction.…”

Section: Introductionmentioning

confidence: 99%

“…The performance loss incurred when detecting compressed signals is a well understood effect for model-driven statistic detectors as, for example, the ones based on maximum likelihood principle [8], [11], [12]. Despite yielding optimum performance, they have a high computational complexity, demand perfect channel state information (CSI), as well as entries of the sensing matrix used in the CS.…”

Section: Introductionmentioning

confidence: 99%

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Compressive Learning in Communication Systems: A Neural Network Receiver for Detecting Compressed Signals in OFDM Systems

2021

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Nowadays, the development of efficient communication system is necessary for future networks. Compressive sensing was proposed as a technique to save storage and energy by compressing signals using simple linear transformations. Although compressed signals can be perfectly recovered, the complexity of the reconstruction operation is high. However, there are applications where compressive signals are processed directly in the compressed domain, with spectrum sensing being an example. Several works apply classical statistical detectors for extracting information from compressed signals, but an emerging concept, denoted as compressive learning, uses machine learning algorithms to extract information from compressed signals and it has promising applications in telecommunications. Compressive learning is being pointed-out as an important technique for future networks, where detecting patterns from a large amount of data is a key feature for new applications. In this paper, we investigate the compressive learning approach applied to spectrum sensing for cognitive radios. We assume that the information about the channel occupancy is collected by spatially distributed sensors and then concentrated in a gateway. The gateway compresses the signals and employs orthogonal frequency division multiplexing to transmit the data to the fusion center, responsible for the final decision about the channel status. We propose a detector based on neural networks to recover information about the occupancy of the channel from the compressed signal and compare it with the optimum maximum likelihood detector, assuming perfect and imperfect channel state information. Results demonstrate that both detectors achieve comparable performance, whereas our proposal has lower complexity.

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Section: B Detection Performance 1) Validation Of Numerical Resultsmentioning

confidence: 99%

“…. , C}, composes a set of classes and that classes occurrences are equiprobable, the MLD decides in favor of the index î that satisfies [8], [11]…”

Section: A Maximum Likelihood Detectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Compressive Learning in Communication Systems: A Neural Network Receiver for Detecting Compressed Signals in OFDM Systems

2021

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“…Yet, the book focuses on the sensing part and does not discuss potential integration with deep learning. Focused survey papers cover compressive sensing applications in different domains, for instance wireless sensor networks (WSNs) [141,154], IoT [28], and EEG processing [44], to name a few. Our survey is orthogonal to these, as we do not focus on a particular domain, but rather merge contributions made in different fields on the common ground of using both the DL techniques and the CS concepts.…”

Section: Survey Rationale and Organizationmentioning

confidence: 99%

Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective

Machidon,

Pejovic

2021

Preprint

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Compressive sensing (CS) is a mathematically elegant tool for reducing the sampling rate, potentially bringing contextawareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally complements CS for adapting the sampling matrix, reconstructing the signal, and learning form the compressed samples. While the CS-DL integration has received substantial research interest recently, it has not yet been thoroughly surveyed, nor has the light been shed on practical issues towards bringing the CS-DL to real world implementations in the ubicomp domain. In this paper we identify main possible ways in which CS and DL can interplay, extract key ideas for making CS-DL efficient, identify major trends in CS-DL research space, and derive guidelines for future evolution of CS-DL within the ubicomp domain. CCS Concepts: • Computer systems organization → Sensor networks; • Human-centered computing → Ubiquitous computing; • Computing methodologies → Neural networks.

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Deep learning for compressive sensing: a ubiquitous systems perspective

Machidon

Pejović

2022

Artif Intell Rev

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Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor sampling rate, potentially bringing context-awareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally complements CS for adapting the sampling matrix, reconstructing the signal, and learning from the compressed samples. While the CS–DL integration has received substantial research interest recently, it has not yet been thoroughly surveyed, nor has any light been shed on practical issues towards bringing the CS–DL to real world implementations in the ubiquitous computing domain. In this paper we identify main possible ways in which CS and DL can interplay, extract key ideas for making CS–DL efficient, outline major trends in the CS–DL research space, and derive guidelines for the future evolution of CS–DL within the ubiquitous computing domain.

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Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey

Cited by 6 publications

References 216 publications

Compressive Learning in Communication Systems: A Neural Network Receiver for Detecting Compressed Signals in OFDM Systems

Compressive Learning in Communication Systems: A Neural Network Receiver for Detecting Compressed Signals in OFDM Systems

Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective

Deep learning for compressive sensing: a ubiquitous systems perspective

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