Journal
SENSORS
Volume 21, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/s21103506
Keywords
cognitive radios; multiband spectrum sensing; machine learning; wavelets; software-defined radio
Funding
- Mexican National Council of Science and Technology (CONACYT) [490180]
- Program for the Professional Development of Teachers (PRODEP)
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This work discusses a novel multiband spectrum sensing technique for cognitive radios using wavelets, machine learning, and fractal dimensions. The proposed method was tested in real-time scenario by linking multiple affordable software-defined radios. The results showed promising performance with a 95% success rate for signal-to-noise ratio values greater than 0 dB.
In this work, a novel multiband spectrum sensing technique is implemented in the context of cognitive radios. This technique is based on multiresolution analysis (wavelets), machine learning, and the Higuchi fractal dimension. The theoretical contribution was developed before by the authors; however, it has never been tested in a real-time scenario. Hence, in this work, it is proposed to link several affordable software-defined radios to sense a wide band of the radioelectric spectrum using this technique. Furthermore, in this real-time implementation, the following are proposed: (i) a module for the elimination of impulsive noise, with which the appearance of sudden changes in the signal is reduced through the detail coefficients of the multiresolution analysis, and (ii) the management of different devices through an application that updates the information of each secondary user every 100 ms. The performance of these linked devices was evaluated with encouraging results: 95% probability of success for signal-to-noise ratio (SNR) values greater than 0 dB and just five samples (mean) in error of the edge detection (start and end) for a primary user transmission.
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