Research Article
Detection Algorithm Based on Eigenvalues of Sampling Covariance Matrix for Satellite Cognitive Network
@INPROCEEDINGS{10.1007/978-3-031-34851-8_4, author={Wenjie Zhou and Dezhi Li and Zhenyong Wang and Qing Guo}, title={Detection Algorithm Based on Eigenvalues of Sampling Covariance Matrix for Satellite Cognitive Network}, proceedings={Wireless and Satellite Systems. 13th EAI International Conference, WiSATS 2022, Virtual Event, Singapore, March 12-13, 2023, Proceedings}, proceedings_a={WISATS}, year={2023}, month={6}, keywords={Satellite cognitive network Spectrum sensing Detection based on sampling covariance eigenvalues}, doi={10.1007/978-3-031-34851-8_4} }- Wenjie Zhou
Dezhi Li
Zhenyong Wang
Qing Guo
Year: 2023
Detection Algorithm Based on Eigenvalues of Sampling Covariance Matrix for Satellite Cognitive Network
WISATS
Springer
DOI: 10.1007/978-3-031-34851-8_4
Abstract
Satellite cognitive network is currently facing a lot of complex spectrum environment with a lot of interference, and the required user signal strength will change with a variety of external factors, which directly affects the above series of results obtained through the decision mechanism and it can not be well applied to satellite cognitive network. So a new blind detection algorithm based on maximum and minimum eigenvalues of sampling covariance matrix is proposed. In this algorithm, the ratio of the difference and sum of the maximum and minimum eigenvalues of the sampling covariance matrix is used as the perceptual decision quantity. Then, by introducing the latest results of the distribution of the maximum and minimum eigenvalues of the sampling covariance matrix in large dimensional random matrix, an effective decision threshold calculation method is designed. Compared with the classical eigenvalue detection algorithm, the new algorithm has the advantage of accurate calculation of perceptual decision threshold, and can effectively improve the detection performance and the reliability of decision results.
