Blind Primary User Signal Detection Algorithm Based on Matrix Eigenvalues

doi:10.13438/j.cnki.jdzk.2020.01.007

Abstract

Abstract:

In the process of multi-antenna primary user signal detection, the maximum eigenvalue and the minimum eigenvalue of the sampling covariance matrix of the received signal are significantly different when the channel is idle and the channel is occupied. According to this observation, a new blind detection algorithm based on the eigenvalues of sampling covariance matrix is proposed. The ratio of the difference and sum of the maximum eigenvalue to the minimum eigenvalue of the sampling covariance matrix is used as the decision statistic. By introducing the latest results on the distribution of maximum eigenvalues and minimum eigenvalues of the sampled covariance matrices in the large-dimensional random matrix theory, an effective method for calculating decision threshold is proposed. Compared with the classical eigenvalue detection algorithm, the new algorithm has the advantage of accurate calculation of the decision threshold, and effectively improves the detection performance and the reliability of decision results. The feasibility and superiority of the new algorithm are verified by Monte Carlo simulation experiments.

Key words: cognitive radio, primary signal, signal detection; , the sample covariance matrix, eigenvalue

HUANG Xiaoyu, TIAN Kun, WANG Xiangmin, YANG Xi. Blind Primary User Signal Detection Algorithm Based on Matrix Eigenvalues[J]. Journal of Jishou University（Natural Sciences Edition), DOI: 10.13438/j.cnki.jdzk.2020.01.007.

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