The research of wideband spectrum sensing algorithm based on multi-resolution singular spectral entropy

Yangyi Chen; Shaojing Su; Peng Wu; Xudong Wen

doi:10.1117/12.2553182

31 January 2020 The research of wideband spectrum sensing algorithm based on multi-resolution singular spectral entropy

Yangyi Chen, Shaojing Su, Peng Wu, Xudong Wen

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 1142744 (2020) https://doi.org/10.1117/12.2553182
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

Abstract

With the rapid development of wireless technology, spectrum resources become more and more scare. In order to solve the shortage of spectrum resources, cognitive radio technology is proposed and spectrum sensing is the key technology of it. At present, wide bandwidth signals have been widely used for data transmission and wideband spectrum sensing has become the research focus. Aiming at this problem, this paper proposes a non-cooperative wideband spectrum sensing algorithm based on multi-resolution singular spectrum entropy. Wavelet packet decomposition technology is used to decompose the wideband signalsinto multiple wavelet packet coefficients and each wavelet packet coefficient represents the signal’s characteristics of different frequency bands. On the basis of that, the singular spectrum entropy analysis is performed on the wavelet packet coefficients in the same channel. After that, the spectrum state information of different channels is obtained. Simulations show that the proposed algorithm has better performance than traditional spectrum sensing algorithm and it improves the detection probability of the signals effectively even at low SNR.

Citation Download Citation

Yangyi Chen, Shaojing Su, Peng Wu, and Xudong Wen "The research of wideband spectrum sensing algorithm based on multi-resolution singular spectral entropy", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 1142744 (31 January 2020); https://doi.org/10.1117/12.2553182

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