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**Chapter 0**

**Chapter 9**

**Data Mining Applied to**

**Cognitive Radio Systems**

http://dx.doi.org/10.5772/51824

communication protocols [14].

vector machine, Bayesian classifiers, etc.

cited.

different learning paradigms.

**1. Introduction**

Additional information is available at the end of the chapter

Lilian Freitas, Yomara Pires, Jefferson Morais, João Costa and Aldebaro Klautau

Cognitive radio (CR) is a novel technology that allows to improve spectrum utilization by enabling opportunistic access to the licensed spectrum band by unlicensed users [2]. This is accomplished through heterogeneous architectures and techniques of dynamic spectrum access. The CR is defined as an intelligent wireless communication system that is aware of its environment and is capable to learn from the environment and adapt its transmission parameters, such as frequency, modulation, transmission power and

An important aspect of a cognitive radio is spectrum sensing [10], which involves two main tasks: signal detection and modulation classification. Signal detection refers to detection of unused spectrum (spectrum holes). It is a simpler task and can be done, for example, by comparing the energy in the frequency band of interest with a predetermined threshold. This task is important so that the unlicensed users do not cause interference to licensed users. Modulation classification consists in automatically identifying the modulation scheme (PSK, FM, QAM, etc) of a given communication system with a high probability of success and in a short period of time. The identification of the modulation scheme allows the cognitive radio to demodulate the received signal. In order to accomplish the task of modulation classification, several data mining techniques can be applied, such as artificial neural networks, support

This chapter aims to evaluate different algorithms for classification of modulation signals on spectrum sensing. The features used for classification are based on a well-established technique called cyclostationarity [7, 10]. Based on these features are evaluated the performances of five data mining techniques: naïve bayes, decision tree, k-nearest neighbor (KNN), support vector machine (SVM) and artificial neural networks (ANN). The choice of such techniques was based on the fact that they are the most popular representatives of

> ©2012 Freitas et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly

©2012 Freitas et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
