Analysis of Spectrum Occupancy Using Machine Learning Algorithms

Azmat, Freeha; Chen, Yunfei; Stocks, Nigel G.

doi:10.1109/tvt.2015.2487047

Cited by 94 publications

(41 citation statements)

References 22 publications

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“…Tpd = Pdn + Pda (13) where Tpd is the total packet drop, Pdn is the total number of dropped packets in the normal mode, and Pda is the dropped packets when the network is threatened.…”

Section: Rsu-l Prevention Mechanismmentioning

confidence: 99%

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Secure Intelligent Vehicular Network Using Fog Computing

Erskine

Elleithy

2019

Electronics

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VANET (vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. The intermittent exchange of real-time safety message delivery in VANET has become an urgent concern due to DoS (denial of service) and smart and normal intrusions (SNI) attacks. The intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication needs. In this research, fog computing (FC) integrates with hybrid optimization algorithms (OAs) including the Cuckoo search algorithm (CSA), firefly algorithm (FA), firefly neural network, and the key distribution establishment (KDE) for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme is termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC). A feedforward back propagation neural network (FFBP-NN), also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The SIVNFC scheme is compared with the Cuckoo, the FA, and the firefly neural network to evaluate the quality of services (QoS) parameters such as jitter and throughput.

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“…Tpd = Pdn + Pda (13) where Tpd is the total packet drop, Pdn is the total number of dropped packets in the normal mode, and Pda is the dropped packets when the network is threatened.…”

Section: Rsu-l Prevention Mechanismmentioning

confidence: 99%

“…In addition, the authors have conducted separate investigations on OAs based upon FC while utilizing DSRC data link technology for data transmission. The authors' investigation involved CSA [10][11][12], FAs [13][14][15] and a firefly neural network [16]. The aim of the authors was to evaluate QoS parameters for delay/jitter and throughput in VANET.…”

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confidence: 99%

Secure Intelligent Vehicular Network Using Fog Computing

Erskine

Elleithy

2019

Electronics

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“…Recently, machine learning algorithms have drawn attention for spectrum sensing. Azmat et al [17] proved that an SVM-based classifier with a firefly algorithm outperforms a naïve Bayesian classifier, a decision tree, a support vector machine, linear regression, and the hidden Markov model when applied to spectrum sensing. Simulation results obtained by Awe and Lambotharan [27] presented a study of multi-class SVM-based cooperative spectrum sensing decision making.…”

Section: Related Workmentioning

confidence: 99%

Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

Jan

Koo

2018

Applied Sciences

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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 power based on the hypothesis result (the energy level of the sensed signal) and the residual energy in the battery: the lower the energy of the received signal, the higher the transmission power, and vice versa. Conversely, the lower is the residual energy in the node, the lower is the transmission power. This technique increases the throughput of a secondary link by providing a higher number of transmission events, compared to the conventional two-class hypothesis. Furthermore, transmission with low power for higher energy levels in the sensed signal reduces the probability of interference with primary users if, for instance, detection was missed. The familiar machine learning algorithm known as a support vector machine (SVM) is used in a one-versus-rest approach to classify the input signal into predefined classes. The input signal to the SVM is composed of three statistical features extracted from the sensed signal and a number ranging from 0 to 100 representing the percentage of residual energy in the node's battery. To increase the generalization of the classifier, k-fold cross-validation is utilized in the training phase. The experimental results show that an SVM with the given features performs satisfactorily for all kernels, but an SVM with a polynomial kernel outperforms linear and radial-basis function kernels in terms of accuracy. Furthermore, the proposed multi-class hypothesis achieves higher throughput compared to the conventional two-class hypothesis for spectrum sensing in cognitive radio networks.

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“…In the context of CR, ML can be effectively utilized for PU traffic prediction, SU capacity maximization, spectrum occupancy modeling, etc. 16 Getting a-priori information about the PU usage characteristics of any channel in a CR scenario is a tedious task that actually limits the efficiency and robustness of most of the cumbersome statistical models. 17 Various ML techniques have been explored for CR applications like channel restoration, spectrum occupancy variation, feature extraction, and spectrum prediction.…”

Section: Existing Work In Temporal Spectrum Predictionmentioning

confidence: 99%

Predictive spectrum occupancy probability‐based spatio‐temporal dynamic channel allocation map for future cognitive wireless networks

Agarwal

Gangopadhyay

2018

Trans Emerging Tel Tech

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For a cognitive radio (CR) user to dynamically access the available primary user channels, the spectrum sensing data is required at various locations; however, the data is not generally available at the points present in between the two spectrum sensors. To obtain this data, it is also not feasible to conduct the spectrum measurement surveys at every location. This calls for an interpolation‐based spectrum occupancy data collection in the space. Moreover, the information about the future primary user activity is necessary for highly enhanced dynamic channel allocation with better quality of experience (QoE) of CR users in terms of improved channel utilization and reduced spectrum sensing energy requirement. Most of the existing works are inclined toward either spatial spectrum interpolation or temporal spectrum prediction. In this work, a novel hybridized approach combining the Kriging‐based statistical spatial interpolation and the recurrent neural networks–based temporal prediction of the spectrum has been proposed to obtain the spectrum occupancy probability information using empirical spectrum measurements data in cellular CR networks. Furthermore, the aforementioned framework has been utilized for dynamic channel allocation with the help of a newly investigated CR channel allocation scheme. The corresponding results have been validated through various CR QoE measures and compared with the existing channel allocation strategies, where it is found that the proposed channel allocation scheme significantly outperforms the other schemes with the enhanced QoE of CR. Finally, a spatio‐temporal channel allocation map has been presented for secondary user to perform its best possible CR operation with efficient dynamic spectrum access.

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Analysis of Spectrum Occupancy Using Machine Learning Algorithms

Cited by 94 publications

References 22 publications

Secure Intelligent Vehicular Network Using Fog Computing

Secure Intelligent Vehicular Network Using Fog Computing

Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

Predictive spectrum occupancy probability‐based spatio‐temporal dynamic channel allocation map for future cognitive wireless networks

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