Adjacency matrix-based OFDM frequency spectrum sensing method

Abstract

The invention discloses an adjacency matrix-based OFDM frequency spectrum sensing method. A power spectrum chart of a received signal is converted into an adjacency matrix after normalization and quantification processing; a submatrix is taken from a top left corner of the determined adjacency matrix; and the sum of all elements of the submatrix is compared with a preset judgment threshold as test statistics to judge whether an OFDM signal exists or not. According to the method, implementation is simple; the computation complexity is low; any prior information of the received signal is not needed; and the adjacency matrix-based OFDM frequency spectrum sensing method still has good detection performance under a low signal-to-noise ratio.

Description

technical field [0001] The invention relates to the technical field of spectrum sensing, in particular to an OFDM spectrum sensing method based on an adjacency matrix. Background technique [0002] With the development of communication technology, people's demand for spectrum resources is increasing day by day. Under the traditional static spectrum allocation strategy, the utilization rate of spectrum is extremely low, which leads to the problem of scarcity of spectrum resources. To solve this problem, Dr. Joseph Mitola proposed the concept of cognitive radio (Cognitive Radio, CR). Cognitive radio is an intelligent new technology to improve spectrum utilization. Its core idea is to enable wireless communication devices to discover "spectrum holes" and use them reasonably, so as to achieve high spectrum utilization and high reliability of communication. Spectrum sensing technology is one of the premise and key technologies of cognitive radio. Spectrum sensing needs to meet ...

AI Technical Summary

Problems solved by technology

[0003] Currently commonly used spectrum sensing methods mainly include matched filter detection, energy detection and cyclostationary feature detection. These three methods have their own advantages and disadvantages: matched filter detection can obtain higher processing gain, but requires timing and frequency synchronization, and It needs to know the prior information of the authorized user signal; the energy detection is simple to implement, has strong flexibility, low computational complexity, and does not require any prior information of the authorized user, but it has poor robustness and is easily affected by noise; cyclostationary characteristics The detection does not need to know any prior information of the authorized user and still has good detection performance, but the calculation complexity is high and the real-time performance is poor

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