2018
DOI: 10.3390/app8030421
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Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

Abstract: A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional two-class hypothesis) is further divided into quantized regions, whereas the hypothesis that the primary user is present is conserved. The non-radio frequency energy harvesting-equiped secondary user transmits, when the primary user is absent, with transmission pow… Show more

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Cited by 20 publications
(21 citation statements)
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“…In [21], authors presented a framework based on the theory of multiclass to exploit the attainable throughput in CRN. Through the absence of the PU in the two-class hypothesis, the energy range of sensing signal is separated into quantized areas while during the presence of the PU, the sensing signal is conserved.…”
Section: Spectrum Sensingmentioning
confidence: 99%
See 3 more Smart Citations
“…In [21], authors presented a framework based on the theory of multiclass to exploit the attainable throughput in CRN. Through the absence of the PU in the two-class hypothesis, the energy range of sensing signal is separated into quantized areas while during the presence of the PU, the sensing signal is conserved.…”
Section: Spectrum Sensingmentioning
confidence: 99%
“…In this approach, a user can concurrently use various vacant channels for transmitting CR. In [21], the authors proposed an M/G/1 queuing model to resolve the spectrum handoff problem. The interarrivals to the M/G/1 queue are modeled and shared various vacant channels, for instance, the overall interarrivals from all CRUs.…”
Section: Reactive Handoffmentioning
confidence: 99%
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“…(33) One successful example of a kernel-based learning algorithm is the SVM, which has been employed in many classification problems. (34)(35)(36)(37)(38) SVMs are machinelearning algorithms proposed by Cortes and Vapnik to categorize data into two groups. (39) The main basic concept of the SVM is to find a hyperplane that can separate two or more different classes of data.…”
Section: Classification Of Mind State Using the Svmmentioning
confidence: 99%