# Double-Threshold Cooperative Spectrum Sensing Algorithm Based on Sevcik Fractal Dimension  
[PERSON]  
1 College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; [EMAIL_ADDRESS] (X.D. ); [EMAIL_ADDRESS] (Z.Y. ); [PERSON]DDRESS] (Y.L.) [PERSON]  
1 College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; [EMAIL_ADDRESS] (Z.Y. ); [PERSON]DDRESS] (Y.L.) [PERSON]  
1 College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; [EMAIL_ADDRESS] (Z.Y. ); [PERSON]DDRESS] (Y.L.) [PERSON]  
1 College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; [EMAIL_ADDRESS] (Z.Y. ); [PERSON]DDRESS] (Y.L.) Footnote 1: email: {[EMAIL_ADDRESS]; Tel. : +86-186-8685-0505  
Footnote 2: email: {[EMAIL_ADDRESS]. for positioning in the presence of correlated observations, was provided by [8]. The test in [8] combines the OR and the majority voting rules at once, recovering all those unlucky detection cases that are not sensed due to strong shadowing. The authors in [9] provided a fair comparison between the soft and hard schemes in terms of energy efficiency. The cyclostationary feature detection method [10] detects the signal in terms of the characteristic of the cyclic frequency in the signal spectral correlation function. Therefore, it needs a larger amount of computation due to the fact that it is subject to the signal spectral correlation function. Recently, fractal geometry has been widely used in signal recognition, digital image processing, and other fields. A spectrum sensing algorithm based on fractal dimension obtains the detection information of spectrum sensing without the prior information of the primary user by using the frequency domain fractal dimension of the signal. Furthermore, it has strong anti-noise ability and low computational complexity [11; 12]. Owing to the above advantages, spectrum sensing algorithms based on fractal dimension has become a research focus. The authors in [13] studied the impact of the range of fractal values, ambient noise levels, and the length of the observation window on the detection result, and concluded that the environment and conditions should be considered when using the fractal dimension method. A spectrum sensing algorithm based on two-dimensional detection was proposed in [14]. Though its computational complexity was low, the detection effect in low SNR environments could not meet expectations. A dynamic cooperative spectrum sensing (CSS) based on box dimension was presented by [15]. However, this method had poor detection accuracy in the low SNR region due to limitations of box dimension. In [16], a CSS method based on the correlation box dimension was proposed, which eliminated the effect of noise by using the signal autocorrelation operation in the local detection and thus achieved better detection effect. Nevertheless, the autocorrelation operation increased its complexity. The cognitive ultra-wideband (CWUB) spectrum sensing was studied in [17], where the fractal box dimension was used for double-threshold CSS and the information dimension was used to identify the signal. However, it is difficult to identify certain signals by information dimension, and the use of two fractal dimensions increases the computational complexity. The Sevcik fractal dimension (SFD) was used in [18], with which the accuracy of local spectrum sensing was improved and the drawback that the box dimension cannot sense the signal of some modulation modes was overcome. Noise power changes with time and physical position in the practical communication environment; moreover, there exists a certain deviation for receivers to estimate noise power. Thus, noise uncertainty can seriously affect the accuracy of spectrum sensing. The proposed algorithm senses the presence of primary users based on analyzing different characteristics of the SFD between signals and noise.