1. Introduction

In this chapter, we study the problem of detection of unoccupied primary user spectrum (i.e., spectrum hole). We also introduce the methods for estimating the time when primary user channel state is available, so that the secondary spectrum user can adjust their transmission strategies accordingly.

The chapter is organized in two parts. The first part of the chapter focuses on the problem of detecting the unoccupied spectrum left by the primary user. In this part, we introduce the usage of machine-learning (ML) techniques as a fusion algorithm in cooperative spectrum sensing based on energy detector [1, 2]. In particular, we train a machine-learning classifier (i.e., K-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), and Decision tree (DT)) over a set containing energy test statistics of PU channel frames along with their

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is 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.

corresponding decisions about the presence or absence of PU transmission in the channel. Then, we use the trained classifier to predict the decisions for newly unseen PU channel frames [3]. The second part focuses on estimating the near future of PU channel state. In the literature, there are many proposals that have studied the problem of estimating PU channel state in cognitive radio (CR) [4–6]. However, most of these studies focused on predicting PU channel state in frequency domain by converting the received digital signals into frequency domain using fast Fourier transform (FFT). This increases the system complexity due to the FFT computations process. In the second part of the chapter, we introduce a new time-domain approach for PU channel state prediction based on time series prediction with some machinelearning prediction model. In particular, a time series is used to capture PU channel state detection sequence (PU channel "idle" or "occupied") in time domain. Then, prediction models such as the hidden Markov model (HMM) and Markov switching model (MSM) are used to predict the behavior of the time series that used capture PU channel state [7].

while M frames are used for training the machine-learning (ML) classifier. The received signal

where sijð Þ n is the PU signal which is assumed to follow Gaussian i.i.d random process (i.e.,

to the fact that all K nodes are sensing the same frame at a given time, the global decision about PU channel availability will be made at the fusion center only. Thus, the energy statistic for

> yijð Þ n �� � � �

Yi is a random variable that has chi-square distribution probability density function (2N degrees of freedom for complex value and with N degrees of freedom for real value case). If we assume that the channel remains unchanged during the observation interval and there are enough number of samples observed ð Þ N ≥ 200 [8], then we can approximate Yi using Gaussian

� � � 2

=N � � H0

2, is the standard deviation of noise samples wijð Þ <sup>n</sup> , and <sup>γ</sup>ij is the observed signal-to-

<sup>2</sup> . For a chosen threshold λ<sup>j</sup> for each frame in the probability of the false alarm, Pf as

noise ratio (SNR) of the ith frame sensed at the j th cooperative node. Assuming that the noise variance and the SNR at the node remain unchanged for all M frames, then γij ¼ γ<sup>j</sup> and

<sup>4</sup> <sup>1</sup> <sup>þ</sup> <sup>γ</sup>ij � �<sup>2</sup>

=N H1

� � (4)

ffiffiffiffiffi

(

yijð Þ¼ n

Yi <sup>¼</sup> <sup>1</sup> N X N

σij 2 ; 2σij 4

8 ><

>:

Pf λ<sup>j</sup>

and the probability of detection Pd is given by

ðσij

n¼1

<sup>2</sup> <sup>1</sup> <sup>þ</sup> <sup>γ</sup>ij � �, <sup>2</sup>σij

� � <sup>¼</sup> Pr Yi <sup>&</sup>gt; <sup>λ</sup>jjH<sup>0</sup> � �

λj σj <sup>2</sup> � 1

ð ∞

λj e �ð Þ <sup>λ</sup>j�σ<sup>j</sup> 2 <sup>=</sup> ffiffi 2 <sup>p</sup> <sup>σ</sup><sup>j</sup> 2

<sup>¼</sup> <sup>1</sup> ffiffiffiffiffiffiffiffiffi 2πσ<sup>j</sup> p

¼ Q

th cooperative node yijð Þ n , 1 ≤ n ≤ N , 1 ≤ i ≤ M , 1 ≤ j ≤K is given by

wijð Þ n H0

(1)

119

(3)

γij p sijð Þþ n wijð Þ n H1

<sup>2</sup> variance), wijð Þ <sup>n</sup> is the noise which is also assumed to follow Gaussian i.i.d

Machine Learning Approaches for Spectrum Management in Cognitive Radio Networks

th cooperative node Yij can be represented by the energy test statistic of the

<sup>2</sup> variance) because sijð Þ <sup>n</sup> and wijð Þ <sup>n</sup> are independent. Due

, 1 ≤ i ≤ M (2)

http://dx.doi.org/10.5772/intechopen.74599

of ith frame at the j

zero mean and σ<sup>s</sup>

the ith frame at the j

distribution as follows:

given in [9] can be written as

where σij

σij <sup>2</sup> <sup>¼</sup> <sup>σ</sup><sup>j</sup>

random process (zero mean and σ<sup>u</sup>

ith frame at the fusion center which is given by

Yi ¼
