Self-adaptive time-frequency hole detection method based on wavelet transformation

Abstract

The invention discloses a self-adaptive time-frequency hole detection method based on wavelet transformation. A wavelet transformation technology is adopted to self-adaptively carry out time frequency domain subsection on time continuous signals or discrete-time signals and judge whether each time frequency resource block is idle or not. The method comprises the following steps of: (1) firstly carrying out time domain segmentation on cognition user monitoring and receiving signals to ensure the occupied frequency band of each main user to be constant in the segment; (2) carrying out Fourier transformation on receiving signals of each sub time interval in the existing method to obtain the signal frequency spectrum of each sub time interval, and carrying out wavelet transformation to the frequency spectrum to detect the border of each frequency range so as to realize frequency range division; and (3) synthesizing self-adaptive time and frequency domain segmentation results in the last two steps to comprehensively analyze each time frequency resource block and judge whether the time frequency resource block is idle or not. The method disclosed by the invention realizes time domain and frequency domain self-adaptive segmentation of signals, effectively solves the contradiction between high resolution of a frequency spectrum hole of the frequency spectrum and the quick and in-time detection requirement in frequency spectrum detection, and simultaneously, is used for estimating the energy distribution of time and frequency domains of main user signals. A simulation result verifies the effectiveness of the method.

Description

Technical field: [0001] The invention relates to a time-frequency cavitation sensing technology applicable to the field of cognitive radio, which adopts a method based on wavelet transform signal processing to adaptively detect the two-dimensional energy distribution in the time domain and frequency domain of the wireless environment to detect Time-frequency holes in wireless environments. Background technique: [0002] Wireless communication spectrum is a limited and precious resource. With the rapid development of information society and economy, the scarcity of spectrum resources has increasingly become an important bottleneck for the realization of mobile communication and broadband wireless access technologies; on the other hand, in stark contrast to the shortage of spectrum resources However, the existing spectrum utilization rate is extremely low. In this context, cognitive radio technology is considered to be one of the potential key technologies of the next generat...

AI Technical Summary

Problems solved by technology

[0004] A lot of research work has been done on cognitive radio spectrum sensing technology and adaptive modulation technology at home and abroad. The research on spectrum sensing technology can be roughly divided into the following two categories: (1) Cooperative detection: by processing multiple sensing users' detection information to more accurately determine whether the primary user exists. This type of method usually relies on complex centralized control or distributed control to realize the detection information exchange of multiple sensing users; The usage status of the spectrum is determined by recognizing whether there is a primary user signal in the signal received by the user. This type of method can be divided into the following four categories: 1) Energy detection method: This type of algorithm usually does not need to know the prior information of the authorized user signal. Can be reliably applied to various unknown channels, fast detection, simple implementation, but this type of algorithm relies on accurate estimation of noise power, in practice, noise power estimation error will lead to higher false alarm probability; 2) feature detection method: this type of algorithm Using the known characteristics of the primary user signal (such as waveform characteristics, cyclostationary characteristics, etc.) to detect the spectrum occupied by the primary user can reliably detect spectral holes in a strong interference environment, but this type of algorithm requires prior knowledge of the primary user signal. 3) Correlation detection algorithm: This type of algorithm uses the characteristic that the signal sample of the main user has a much higher correlation than the noise sample to realize the detection of the authorized user, but this 4) wavelet transform spectrum detection method: this type of algorithm can flexibly adjust the spectrum detection resolution by using wavelet decomposition or wavelet packet decomposition to achieve multi-resolution spectrum detection, or use The sudden feature extraction ability of wavelet change can detect the sudden change of the main user occupancy-release spectrum, but this kind of algorithm usually has the disadvantage of high algorithm complexity

[0005] At present, existing cognitive radio spectrum sensing methods at home and abroad are mainly aimed at the detection of "spectral holes" in the frequency domain, but rarely detect "time-frequency holes" from the perspective of two-dimensional time-frequency analysis in the time domain and frequency domain, which makes it difficult Estimating Time-Domain Properties of Idle Spectrum

In order to obtain a higher frequency resolution, this type of spectrum detection method requires sampling at a longer time interval. On the other hand, in order to detect the spectrum hole in time and the primary user reoccupied the spectrum hole, a shorter time sampling interval is required. Therefore, in addition to the limitations mentioned in the previous section, the existing spectrum detection methods are also limited by the contradiction between the longer sampling interval and the fast and timely detection requirements.

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