A Novel Multiband Spectrum Sensing Method Based on Wavelets and the Higuchi Fractal Dimension

Abstract

In this work, two novel methodologies for the multiband spectrum sensing in cognitive radios are implemented. Methods are based on the continuous wavelet transform (CWT) and the multiresolution analysis (MRA) to detect the edges of available holes in the considered wideband spectrum. Besides, MRA is also combined with the Higuchi fractal dimension (a non-linear measure) to establish the decision rule permitting the detection of the absence or presence of one or multiple primary users in the studied wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results present these two methods as effective options for detecting primary user activity on the multiband spectrum. The first methodology works for 95% of cases, while the second one presents 98% of effectivity under simulated signals of signal-to-noise ratios (SNR) higher than 0 dB.

Machine Learning Techniques Applied to Multiband Spectrum Sensing in Cognitive Radios

“…The real signals mentioned in Section 4.3 of [10] were again used to test the performance of different proposed methodologies. These signals were obtained from a whole band varying from 0.6 GHz to 2.6 GHz.…”

“…First, it is presented a short summary of the technique developed by the authors in [10]. Basically, this original methodology considers the multiresolution analysis (a wavelet-based dyadic filter bank) and the Higuchi fractal dimension to detect transmissions of PUs.…”

“…Authors of this paper presented a previous work describing how to detect efficiently PU presence by considering new alternative methods [10], based on hybrid linear and non-linear techniques. In this contribution, a couple of MSS algorithms based on multiresolution analysis (MRA) and the Higuchi fractal dimension (HFD) were proposed and discussed.…”

“…However, there are several basic issues which still represent a challenge, for example, the threshold setting for distinguishing PU presence or absence [11] and the new paradigm to convert CR in an really smart entity [12]. In [10], the detection threshold of PUs was manually placed, derived from the results obtained with simulated signals. Nevertheless, considering the high variability of the spectrum occupancy, this option was not optimal.…”

Application and Optimization of Wavelet Transform Filter for North-Seeking Gyroscope Sensor Exposed to Vibration

“…The high-frequency coefficients are limited according to the corresponding threshold criteria, and then reconstructed with low-frequency coefficients to achieve the purpose of de-noising and correction [13,14]. With the development of the WT filter, the application field has been greatly expanded to include biomedicine [15,16,17], geophysics [18,19], image processing [20,21], tracking detection [22,23,24], and feature extraction [25,26,27]. The wavelet filter also has precedents for signal processing of gyroscope sensors of the strapdown inertial navigation system (SINS) used for attitude determination, such as the fiber optic gyroscope (FOG) [28,29] and micro electro mechanical system (MEMS) gyroscopes [30,31], for which good application results have been achieved in previous studies [32,33,34,35].…”