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Spectrum_Sensing_in_Mobile_Cognitive_Radio_WIC2021.pdf
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- Abstract
- Cognitive radio (CR) is the solution to the spectrum scarcity issue faced in wireless communications. Spectrum sensing is a crucial enabler for CR because it gives an insight into the free spectrum. In mobile environments, spectrum sensing is more critical as the spectrum occupancy becomes more dynamic with respect to time and frequency. It would be a good idea to exploit some knowledge about the mobility parameters to make better sensing decisions. In this work, we consider a mobile CR scenario where some fixed primary users (PU) transmit in the same frequency band, each having its coverage area. A secondary user (SU), the CR node, is moving and continuously looking for an opportunity to use this band. We explore the possibility of using the SU mobility patterns to improve the spectrum sensing. We do that by using the Bayesian changepoint detection approach and assuming that the mobility parameters can be summarized in some a priori knowledge on the average time of spectrum change. Considering that in CR, the power of the signal to be detected is often unknown, we have introduced a low-complexity algorithm that does not rely on this knowledge, in a previous publication. The comparisons with existing algorithms in the literature have shown that the derived algorithm outperforms its non-Bayesian equivalent at low signal to noise ratio (SNR). The current work extends our previous one, presented in IEEE VTC-2021 Spring conference, in several ways. We go further in the comparisons by studying the computational complexity of the proposed algorithm. Moreover, we investigate different ways of computing the optimal detection threshold in practical scenarios of unknown-SNR.
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Citations
Chédé, A., Louveaux, J., Dossou, M., & Louveaux, J. (2021). Spectrum Sensing in Mobile Cognitive Radio: A Bayesian Changepoint Detection Approach. 41st WIC Symposium on Information Theory and Signal Processing in the Benelux (SITB 2021). https://hdl.handle.net/2078.5/223927