Multi-antenna spectrum sensing method applicable to high-dimension finite sample conditions

Abstract

The invention relates to a multi-antenna spectrum sensing method applicable to high-dimension finite sample conditions. By adopting relevance between multi-antenna receiving signal components to structure sensing decision and sensing decision threshold based on the random stochastic matrix, the multi-antenna spectrum sensing method includes firstly, continuously sampling multi-antenna receiving signals to form a receiving signal data matrix X; then, calculating relevance measurement indicators among the multi-antenna receiving signal components on this basis, and calculating to obtain sensing decision I; secondly, calculating sensing decision threshold t on the basis of the random stochastic matrix; finally, implementing sensing decision, to be specifically, judging that no spectrum hole exists when the sensing decision I is larger than the preset threshold t, or otherwise, judging that a spectrum hole exists. The multi-antenna spectrum sensing method has the advantages that the method is simple and low in calculation complexity in sensing application of the high-dimension finite sample capacity, efficient total blindness detection under the condition of deficiency in statistical information of master user signals, wireless channels and noise can be realized, and sensing results are reliable and the like.

Description

technical field [0001] The invention relates to a spectrum sensing method applied in a large-scale multi-antenna cognitive radio system, and belongs to the technical field of cognitive radio in wireless communication. Background technique [0002] Multi-antenna cognitive radio technology is a research hotspot in the field of wireless communication, and an effective multi-antenna spectrum sensing algorithm is one of the key factors to realize this technology. In the multi-antenna spectrum sensing scenario, the dimension M of the received data vector is numerically equal to the number of antennas, and the sample size N (that is, the number of received data vectors) is determined by the number of times the received signal is sampled within the sensing time. In the traditional multi-antenna cognitive radio system, the number M of antennas configured on the sensing nodes is often very small, so the research of the traditional multi-antenna spectrum sensing method focuses on the a...

AI Technical Summary

Problems solved by technology

This method has the advantages of simple implementation, low computational complexity, efficient full-blind detection under the condition of lack of statistical information of the main user signal, wireless channel and noise, and reliable sensing results. It can be well applied to solve large-scale multi-antenna Spectrum hole detection problem in high-dimensional finite samples such as cognitive radio systems

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