Frequency spectrum detection method based on characteristic cyclic frequency

Abstract

The invention relates to a frequency spectrum detection method based on characteristic cycle frequency, belonging to the technical field of wireless communication. When carrying out cycle spectrum processing of finite length to signals, the invention only carries out processing to cycle spectrum value at the characteristic cycle frequency of signals; simultaneously, the purpose of controlling detection probability and false-alarm probability of the signals is achieved by the adjustment of decision threshold. Based on the basic theory of cycle spectrum detection, the invention adopts finite signal length and selects the characteristic cycle frequency of signals to carry out frequency spectrum detection; when the cycle spectrum value at the characteristic cycle frequency is larger than preset threshold, the frequency spectrum is considered to be occupied or is considered to not be occupied. The proposal greatly improves the frequency spectrum detection performance and solves the problem that frequency detection is carried out under very low signal-to-noise ratio.

Description

technical field [0001] The invention relates to a detection method in the technical field of wireless communication, in particular to a frequency spectrum detection method based on a characteristic cycle frequency. Background technique [0002] Cognitive radio technology makes it possible for secondary users to access authorized primary user PU frequency bands, and correctly detecting whether certain frequency bands are occupied by primary users is one of the core technologies of cognitive radio. In the field of spectrum detection, energy detection and cyclic spectrum detection algorithms are proposed. However, in wireless communication, the location of the user may encounter deep fading, "hidden site", etc., and the signal-to-noise ratio received by the secondary user is very low, and energy detection is greatly limited in this case , but the cyclic spectrum detection technology can achieve better detection performance at low signal-to-noise ratios. However, cyclic spectru...

AI Technical Summary

Problems solved by technology

However, in wireless communication, the location of the user may encounter deep fading, "hidden site", etc., and the signal-to-noise ratio received by the secondary user is very low, and energy detection is greatly limited in this case , but the cyclic spectrum detection technology can achieve better detection performance at low signal-to-noise ratios. However, cyclic spectrum calculations on the full plane require a large amount of calculations, which cannot meet the real-time requirements of spectrum detection.

Although there are many detection methods for spectrum detection, the proposed methods do not effectively solve the real-time detection problem under low signal-to-noise ratio.

[0003] After searching the existing technology, it is found that none of the existing detection schemes can solve the problem of spectrum detection under low SNR, such as the US Patent No. "US 2008 / 0080604A1", Youngsik Hur, Chang-HoLee, etc. In its patent "SPECTRUM-SENSING ALGORITHMS AND METHODS" (the Chinese name is "spectrum detection algorithm and method"), the detection is divided into two steps: rough detection and fine detection to improve detection efficiency, but its detection method is still based on the traditional The energy detection does not fundamentally solve the deficiency of the energy detection itself

In the patent of Chinese invention with the application number "200610020800.5", the applicant is the University of Electronic Science and Technology of China, and the invention name is "A Distributed Spectrum Detection Method Based on Reliability", a method of assigning reliability to distributed nodes is proposed. However, its single node is still based on energy detection, which fails to solve the problem that energy detection is greatly affected by noise.

Although the cyclic spectrum of the cyclostationary signal can not be affected by noise, many related papers have also pointed out that the cyclic spectrum has incomparable advantages over traditional detection methods at low signal-to-noise ratios, but due to the complexity of cyclic spectrum processing, it has been Failed to apply the cyclic spectrum characteristics of the signal to the actual system

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