Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks

Thilina, Karaputugala Madushan; Choi, Kae Won; Saquib, Nazmus; Hossain, Ekram

doi:10.1109/jsac.2013.131120

Cited by 366 publications

(185 citation statements)

References 19 publications

Supporting

Mentioning

182

Contrasting

Unclassified

Order By: Relevance

“…It was concluded that increasing the number of samples per sensing slot improved the accuracy of the classifier. Thilina et al [28] compared the performances of unsupervised and supervised learning techniques in cooperative spectrum sensing. In the family of supervised learning techniques, the SVM-based and the weighted k-nearest neighbor-based classifiers were recommended due to high receiver operating characteristic performance and, in some applications, low training and classification time.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Literature [12] proposed a spectrum sensing method combining support vector machine and MME. The literature [13] analyzes the spectrum sensing performance under different clustering algorithms. Compared with the traditional spectrum sensing method, the spectrum sensing method based on machine learning is more adaptive and does not need to know the prior knowledge of the sensing environment.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

A Spectrum Sensing Method Based on Empirical Mode Decomposition and K-Means Clustering Algorithm

Wang

Zhang

Wan

et al. 2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

To solve the problems of poor performance of traditional spectrum sensing method under low signal-to-noise ratio, a new spectrum sensing method based on Empirical Mode Decomposition algorithm and K-means clustering algorithm is proposed. Firstly, the Empirical Mode Decomposition algorithm and the wavelet threshold algorithm are used to remove the noise components in the spectrum sensing signal, and K-means clustering algorithm is used to determine whether the primary user exists. The method can remove the redundant components such as noise in the nonstationary or nonlinear sampling signal in the real environment and does not need to know the prior information such as signal, channel, and noise, so it can well handle the complicated sensing signal in real environment. This method can reduce the impact of noise on the spectrum sensing system and thus can improve the sensing performance of the system. In the experimental part, the difference between maximum and minimum eigenvalues and the difference between the maximum eigenvalue and the average energy in the random matrix are selected as signal features. Experiments also show that the proposed method is better than the traditional spectrum sensing methods.

show abstract

“…Researchers suggested many models co-operative spectrum sensing [29]- [31] is very much popular in which two type of users, primary user and secondary user. Both collaborate with each other for spectrum sharing with the help of active and inactive phase.…”

Section: A 5g and Cognitive Radiomentioning

confidence: 99%

“…For implementation of cognitive radio a tool is needed, machine learning [31] is pioneer candidate among all of them because it learn from past data and derive knowledge base from it and able to take decision with any manual help. In 2013, paper [31] named "Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks" which discuss all major machine learning algorithms and how they can be used for spectrum sensing in cognitive radio network. After publishing of this paper research community showed interest for using machine learning algorithm for spectrum sensing.…”

Section: A 5g and Cognitive Radiomentioning

confidence: 99%

Modified Random Forest Approach for Resource Allocation in 5G Network

De¹,

Singh²

2016

ijacsa

View full text Add to dashboard Cite

Abstract-According to annual visual network index (VNI)report by the year 2020, 4G will reach its maturity and incremental approach will not meet demand. Only way is to switch to newer generation of mobile technology called as 5G.Resource allocation is critical problem that impact 5G Network operation critically. Timely and accurate assessment of underutilized bandwidth to primary user is necessary in order to utilize it efficiently for increasing network efficiency. This paper presents a decision making system at Fusion center using modified Random Forest. Modified Random Forest is first trained using Database accumulated by measuring different network parameters and can take decision on allocation of resources. The Random Forest is retrained after fixed time interval, considering dynamic nature of network. We also test its performance in comparison with existing AND/OR logic decision logic at Fusion Center

show abstract

Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks

Cited by 366 publications

References 19 publications

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

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

A Spectrum Sensing Method Based on Empirical Mode Decomposition and K-Means Clustering Algorithm

Modified Random Forest Approach for Resource Allocation in 5G Network

Contact Info

Product

Resources

About