Cooperative Modulation Classification of multiple signals in Cognitive Radio Networks

The continuous growth of demand experienced by wireless networks creates a spectrum availability challenge. Cognitive radio (CR) is a promising solution capable of overcoming spectrum scarcity. It is an intelligent radio technology that may be programmed and dynamically conﬁgured to avoid interference and congestion in cognitive radio networks (CRN). Spectrum sensing (SS) is a cognitive radio life cycle task aiming to detect spectrum holes. A number of innovative approaches are devised to monitor the spectrum and to determine when these holes are present. The purpose of this survey is to investigate some of these schemes which are constructed based on machine learning concepts and principles. In addition, this review aims to present a general classiﬁcation of these machine learningbased schemes

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SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks

The Scientific World JOURNAL ◽

10.1155/2014/395212 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Yao Wang ◽

Zhongzhao Zhang ◽

Lin Ma ◽

Jiamei Chen

Keyword(s):

Cognitive Radio ◽

Cognitive Radio Networks ◽

High Speed ◽

Radio Networks ◽

Support Vector ◽

Mobility Prediction ◽

Prediction Scheme ◽

Spectrum Mobility ◽

Short Time

Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements.

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Data Dissemination and Channel Selection in Cognitive Radio Networks

Advances in Wireless Technologies and Telecommunication - Evolution of Cognitive Networks and Self-Adaptive Communication Systems ◽

10.4018/978-1-4666-4189-1.ch002 ◽

2013 ◽

pp. 30-49 ◽

Cited By ~ 1

Author(s):

Mubashir Husain Rehmani ◽

Yasir Faheem

Keyword(s):

Cognitive Radio ◽

Cognitive Radio Networks ◽

Data Dissemination ◽

Channel Selection ◽

Radio Networks ◽

Selection Strategies ◽

Detailed Classification ◽

Pros And Cons ◽

In The Beginning

In this chapter, the authors provide a comprehensive review of broadcasting and channel selection strategies for wireless cognitive radio networks. In the beginning, some applications of the data dissemination in wireless cognitive radio networks are discussed to highlight their importance and utility. Next, the authors provide a detailed classification of broadcasting protocols in light of the existing literature, and the pros and cons of each classified category are discussed. Afterwards, the data dissemination is briefly discussed in the context of multi-channel environments and the related issues are highlighted. Then, the authors discuss the challenges of data dissemination in cognitive radio networks, followed by the classification of channel selection strategies along with their advantages and disadvantages for various classes of applications. In the last part, the authors conclude this chapter with open research issues that need to be addressed to provide efficient channel selection and data dissemination strategies in cognitive radio networks.

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