Search results for "spectrum sensing"

showing 7 items of 7 documents

Contextual neural-network based spectrum prediction for cognitive radio

2015

Cognitive radio is the technique of effective electromagnetic spectrum usage important for future wireless communication including 5G networks. Neural networks are nature-inspired computational models used to solve cognitive radio prediction problems. This paper presents the use of contextual Sigma-if neural network in prediction of channel states for cognitive radio. Our results indicate that Sigma-if neural network confirms better predictions than Multilayer Perceptron (MLP) network and decreases sensing time for the benefit of the increase of the effectiveness of e-m spectrum usage.

Cognitive modelComputational modelArtificial neural networkspectrum sensingbusiness.industryTime delay neural networkComputer scienceComputer Science::Neural and Evolutionary Computationartificial intelligenceCognitive networkMachine learningcomputer.software_genrecontextual predictionCognitive radioMultilayer perceptron5G communicationcontextual processingWirelessArtificial intelligencebusinesscomputer2015 Fourth International Conference on Future Generation Communication Technology (FGCT)
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Random Interruptions in Cooperation for Spectrum Sensing in Cognitive Radio Networks

2015

In this paper, a new cooperation structure for spectrum sensing in cognitive radio networks is proposed which outperforms the existing commonly-used ones in terms of energy efficiency. The efficiency is achieved in the proposed design by introducing random interruptions in the cooperation process between the sensing nodes and the fusion center, along with a compensation process at the fusion center. Regarding the hypothesis testing problem concerned, first, the proposed system behavior is thoroughly analyzed and its associated likelihood-ratio test (LRT) is provided. Next, based on a general linear fusion rule, statistics of the global test summary are derived and the sensing quality is cha…

FOS: Computer and information sciencesSemidefinite programmingMathematical optimizationta213Computer scienceInformation Theory (cs.IT)Computer Science - Information Theory010401 analytical chemistrydecision/data fusion020206 networking & telecommunications02 engineering and technology01 natural sciencesStatistical power0104 chemical sciencescooperative spectrum sensingCognitive radionon-ideal reporting channelsefficiency0202 electrical engineering electronic engineering information engineeringcognitive radio (CR)False alarmElectrical and Electronic EngineeringStatistical hypothesis testingEfficient energy use
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The Max-Product Algorithm Viewed as Linear Data-Fusion: A Distributed Detection Scenario

2019

In this paper, we disclose the statistical behavior of the max-product algorithm configured to solve a maximum a posteriori (MAP) estimation problem in a network of distributed agents. Specifically, we first build a distributed hypothesis test conducted by a max-product iteration over a binary-valued pairwise Markov random field and show that the decision variables obtained are linear combinations of the local log-likelihood ratios observed in the network. Then, we use these linear combinations to formulate the system performance in terms of the false-alarm and detection probabilities. Our findings indicate that, in the hypothesis test concerned, the optimal performance of the max-product a…

FOS: Computer and information sciencesfactor graphsComputer scienceComputer Science - Information TheoryMarkovin ketjut02 engineering and technologyMarkov random fieldsalgoritmit0202 electrical engineering electronic engineering information engineeringMaximum a posteriori estimationmax-product algorithmElectrical and Electronic EngineeringLinear combinationStatistical hypothesis testingdistributed systemsMarkov random fieldspectrum sensingApplied MathematicsNode (networking)Information Theory (cs.IT)linear data-fusionApproximation algorithm020206 networking & telecommunicationsComputer Science Applicationssum-product algorithmPairwise comparisonRandom variableAlgorithmstatistical inference
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Optimization of Linearized Belief Propagation for Distributed Detection

2020

In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a pr…

hajautetut järjestelmätComputer scienceInference02 engineering and technologyBelief propagation01 natural sciencesMarkov random fieldsalgoritmit0202 electrical engineering electronic engineering information engineering0101 mathematicsElectrical and Electronic Engineeringtilastolliset mallitdistributed systemsbelief-propagation algorithmRandom fieldMarkov chainspectrum sensingverkkoteoriasignaalinkäsittely010102 general mathematicslinear data-fusionApproximation algorithm020206 networking & telecommunicationsCognitive radioblind signal processingAlgorithmWireless sensor networkRandom variablestatistical inference
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Joint local quantization and linear cooperation in spectrum sensing for cognitive radio networks

2014

—In designing cognitive radio networks (CRNs), protecting the license holders from harmful interference while maintaining acceptable quality-of-service (QoS) levels for the secondary users is a challenge effectively mitigated by cooperative spectrum sensing schemes. In this paper, cooperative spectrum sensing in CRNs is studied as a three-phase process composed of local sensing, reporting, and decision/data fusion. Then, a significant tradeoff in designing the reporting phase, i.e., the effect of the number of bits used in local sensing quantization on the overall sensing performance is identified and formulated. In addition, a novel approach is proposed to jointly optimize the linear soft-…

non-ideal reporting channelcognitive radio (CR)quantizationdecision fusioncooperative spectrum sensing
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Cooperative spectrum sensing schemes for future dynamic spectrum access infrastructures

2016

spectrum sensingtehokkuusdecision/data fusioncooperative communicationsdynamic spectrum access (DSA)matemaattinen optimointitiedonsiirtononlinear optimizationlangaton tiedonsiirtoradioverkotoptimointiefficiencycognitive radio (CR)taajuusalueetkognitiivinen radiolangattomat verkot
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Linear fusion of interrupted reports in cooperative spectrum sensing for cognitive radio networks

2015

Interrupted reporting has recently been introduced as an effective method to increase the energy efficiency of cooperative spectrum sensing schemes in cognitive radio networks. In this paper, joint optimization of the reporting and fusion phases in a cooperative sensing with interrupted reporting is considered. This optimization aims at finding the best weights used at the fusion center to construct a linear fusion of the received interrupted reports, jointly with Bernoulli distributions governing the statistical behavior of the interruptions. The problem is formulated by using the deflection criterion and as a nonconvex quadratic program which is then solved for a suboptimal solution, in a…

ta113Mathematical optimizationFusionta213Artificial neural networkComputer sciencedecision fusioncooperative spectrum sensingBernoulli's principleCognitive radionon-ideal reporting channelscorrelationcognitive radio (CR)Quadratic programmingEfficient energy use2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
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