Combination algorithm of spectrum sensing in vehicle network based on neural network

doi:10.7523/j.issn.2095-6134.2021.04.015

Abstract

Abstract: In this paper, a multi-conditional spectrum sensing combination algorithm based on neural network is proposed to address the current shortage of spectrum resources in vehicular network. The algorithm combines signal energy, the maximum-minimum of eigenvalues, traces, and the average eigenvalue of the covariance matrix as neural network characteristic parameters, which are achieved through the strong multi-classification ability of neural network. To improve the successful rate of spectrum sensing and the utilization rate of the spectrum, we focus on analyzing the selection of parameter in theory as well as the low signal-to-noise ratio caused by channel fading and shadow effect. Meanwhile, the Doppler effective caused by car moving is also our consideration. Under low signal-to-noise ratio, the simulation results show that the proposed algorithm has better detection performance than existing spectrum sensing algorithms.

Key words: cognitive radio, vehicular network, spectrum sensing, neural network, low signal-to-noise ratio

CLC Number:

TN929

JI Yufeng, ZHENG Min, TAN Chong, LIU Hong. Combination algorithm of spectrum sensing in vehicle network based on neural network[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(4): 549-556.

References

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