Centralized sub-Nyquist wideband spectrum sensing for cognitive radio networks over fading channels

Hoyos, Evelio Astaiza; Parra, Octavio José Salcedo; Muñoz, Wilmar Yesid Campo

doi:10.1016/j.comcom.2020.02.039

Cited by 6 publications

(1 citation statement)

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“…Among these techniques, sensing techniques based on Nyquist and sub-Nyquist have shown promise, as some authors point out in [18]. In Nyquist wideband sensing techniques, the received signal at some point is sampled by a traditional analog-to-digital converter with a Nyquist sampling rate ( f s > 2 f m ) [19]; as an advantage, these techniques present a simple structure, and as disadvantages, high sampling rate and energy cost [20]. The sub-Nyquist-based techniques overcome the disadvantages presented in Nyquist-based SS by reducing the sampling rate and then detecting spectral holes with the partial data that remained [21]; as an advantage, these techniques present a low sampling rate, and as disadvantage, they are sensitive to design imperfections [22].…”

Section: Introductionmentioning

confidence: 99%

Deep Cooperative Spectrum Sensing Based on Residual Neural Network Using Feature Extraction and Random Forest Classifier

Valadao

Amoedo

Costa

et al. 2021

Sensors

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Some bands in the frequency spectrum have become overloaded and others underutilized due to the considerable increase in demand and user allocation policy. Cognitive radio applies detection techniques to dynamically allocate unlicensed users. Cooperative spectrum sensing is currently showing promising results. Therefore, in this work, we propose a cooperative spectrum detection system based on a residual neural network architecture combined with feature extractor and random forest classifier. The objective of this paper is to propose a cooperative spectrum sensing approach that can achieve high accuracy in higher levels of noise power density with less unlicensed users cooperating in the system. Therefore, we propose to extract features of the sensing information of each unlicensed user, then we use a random forest to classify if there is a presence of a licensed user in each band analyzed by the unlicensed user. Then, information from several unlicensed users are shared to a fusion center, where the decision about the presence or absence of a licensed user is accomplished by a model trained by a residual neural network. In our work, we achieved a high level of accuracy even when the noise power density is high, which means that our proposed approach is able to recognize the presence of a licensed user in 98% of the cases when the evaluated channel suffers a high level of noise power density (−134 dBm/Hz). This result was achieved with the cooperation of 10 unlicensed users.

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