Wireless Communications and Mobile Computing

Volume 2018, Article ID 6104502, 10 pages

https://doi.org/10.1155/2018/6104502

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

^{1}School of Automation, Guangdong University of Technology, Guangzhou 510006, China^{2}Key Laboratory of Machine Intelligence and Advanced Computing of the Ministry of Education, Guangzhou 510006, China

Correspondence should be addressed to Yonghua Wang; moc.361@hywzjs

Received 22 April 2018; Accepted 21 June 2018; Published 10 July 2018

Academic Editor: Donatella Darsena

Copyright © 2018 Yonghua Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

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.

#### 1. Introduction

Spectrum sensing technology is the key technology of cognitive radio. The main purpose is to detect spectrum holes and improve spectrum utilization. Conventional spectrum sensing methods include energy detection, matched filter, and the cyclostationary feature detection [1–4]. The perceived signals received in the actual environment include noise, which can affect the sensing performance of traditional spectrum sensing methods [5, 6].

Spectrum sensing based on random matrix is a cooperative spectrum sensing method. This method does not require prior knowledge of sampling signals and can reduce the interference of various fading factors in the environment. Therefore, the method of random matrix theory is gradually applied to spectrum sensing. Literature [7] has extracted a perception method based on the Difference between maximum and minimum eigenvalue (DMM). This method uses DMM as the test statistic and then makes a decision by calculating the threshold. However, the algorithm is susceptible to low signal-to-noise ratio (SNR) and few collaborative users, which will greatly affect the system performance. The literature [8] proposed the Maximum to Minimum Eigenvalue (MME) spectrum sensing method. The method uses the MME eigenvalue as the test statistic to reduce the influence of noise uncertainty, but only when the number of samples is high does it have good sensing performance. Literature [9] proposed a spectrum sensing method based on the Difference between Maximum Eigenvalue and the Average Energy (DMEAE). This method uses DMEAE eigenvalue as the test statistic and derive the corresponding threshold to make a decision. Since the statistical covariance of the received signal and noise are usually different, they can be used to distinguish between the presence of the primary user signal and the presence of noise only. However, the traditional method of random matrix theory will have the problem of inaccurate estimation of the threshold. At the same time, in the complex real environment, the SNR of the primary user signal is low and susceptible to the multipath fading and time diffusion of the wireless channel, which will greatly affect the system performance.

In order to solve the problem of inaccurate threshold derivation in traditional stochastic matrix theory, the literature [10] proposed spectrum sensing method based on signal eigenvalue and clustering algorithm. This method uses a K-means clustering algorithm and signal eigenvalues to train a classifier and use the classifier to determine whether the primary user signal exists. Spectrum sensing can also be considered as a problem of two classifications (the existence of a primary user or the absence of a primary user). Because the clustering algorithm can deal with the two classification problems well, the spectrum sensing method based on machine learning has gradually become a focus of people’s research. Literature [11] proposed a spectrum sensing method based on K-means clustering. This method takes the energy of the signal as a feature and then divides this feature into a channel available class or a channel unavailable class through a K-means clustering algorithm. 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. However, taking into account the actual environment, the signal often contains complex uncertain noises which will affect the effectiveness of the clustering algorithm, making the spectrum sensing system have poor clustering performance at low SNR and it will affect the sensing performance of the entire system.

In a cognitive radio network, noise in the actual environment will affect the sensing performance of the system. In order to improve the sensing performance of the system, it is necessary to remove redundant information such as noise in the spectrum signal. However, most signals received by the system are nonstationary and nonlinear. In order to deal with nonstationary and nonlinear signals, the traditional methods use short-time Fourier transform, Wigner-Ville distribution, and wavelet transform. However, these methods are essentially Fourier transforms, which are subject to indefinite principles. Therefore, there are certain defects in the traditional signal processing methods. Literature [14] proposed an Empirical Mode Decomposition (EMD) algorithm that decomposes complex signals into a collection of Intrinsic Mode Functions (IMF). Literature [15] proposed a spectrum sensing method based on Hilbert-Huang Transform (HHT). First, the spectrum signal is subjected to EMD decomposition to obtain the IMF component. Calculate the Hilbert spectrum and total HHT spectrum for each IMF component and then find the total marginal spectrum. Compare the total marginal spectrum with the threshold to determine whether the primary user exists.

In order to make the system have good adaptability in the actual environment, reduce the impact of noise and accurately determine whether the main user exists. Based on previous studies, we propose a spectrum sensing method (KEMDSS) based on EMD algorithm and K-means clustering algorithm. The KEMDSS method will uses EMD algorithm and wavelet threshold algorithm to process the received spectrum signal, removing the redundant information in the signal, and then use the K-means clustering algorithm to determine whether the primary user exists. To verify the effectiveness of the KEMDSS method, this article uses DMM and DMEAE as signal features. The experimental results also show that KEMDSS method can improve the sensing performance of the system no matter which feature is used.

#### 2. System Model

The KEMDSS method is mainly composed of a signal processing part and a clustering algorithm part. In order to be able to deal with complex spectrum signals in the actual environment, we posed a signal processing method (WEMD) which combines the EMD algorithm and the wavelet threshold algorithm. According to the multiscale filtering characteristics of EMD, the spectrum signal can be decomposed into several high-frequency and low-frequency IMF components. Considering signal noise and other interference factors are mainly concentrated in the high-frequency band, the WEMD method uses wavelet algorithm to remove the noise in high-frequency IMF components. The purpose is to reduce the impact of noise and other factors on the system. The clustering algorithm mainly uses the DMEAE and DMM signal features, collects a certain amount of training sets and test sets, and then uses the K-means clustering algorithm to train the classifiers for judgment, thereby determining whether the primary user exists. The entire KEMDSS flowchart is as shown in Figure 1.