Frequency spectrum detection method based on channel holding time

Abstract

The invention discloses a frequency spectrum detection method based on channel holding time. The method includes: step 1, using a time model of the Poisson Pareto process, reducing a Web application program and the additive Gaussian white noise process, and calculating detection probability through detection probability of energy; step 2, on the basis of time and noise statistics, inferring detection time delta t of a main user; step 3, on the premise that throughput reaches target throughput, optimizing the detection time delta t to enable the same to be minimum. Throughput of cognitive users is maximized by optimizing the detection time, and energy sources of cognitive radio are saved; an expression of minimum detection time required to acquire probability of 90% detection main users is summarized by utilizing an expression of detection probability, so that performance of a cognitive radio network can be remarkably improved by optimizing the detection time.

Description

technical field [0001] The invention belongs to the technical field of cognitive radio, and in particular relates to a frequency spectrum detection method based on channel holding time. Background technique [0002] The radio spectrum is a limited natural resource. The wide use of wireless communication leads to the scarcity of the spectrum, which makes the spectrum increasingly crowded. At the same time, people find that many static spectrums that have been allocated are ineffective or underutilized, resulting in waste of spectrum resources. Traditional spectrum management methods are inflexible, and it is difficult to find vacant frequency bands in specific frequency bands with licenses from wireless operators. In this case, we need an improved spectrum utilization, which will create opportunities for Dynamic Spectrum Access (DSA). Among them, one possible solution is to use "cognitive radio" technology. [0003] The traditional method is to use the method of hypothesis ...

AI Technical Summary

Problems solved by technology

First of all, in a cognitive radio system, it is unrealistic to continuously sense the spectrum, because it will occupy all the receiver resources, and there is no space to send and receive data, resulting in a waste of resources

Secondly, energy detection cannot work under low signal-to-noise ratio, and users cannot be distinguished; both cyclostationary detection and matched filter detection require partial information of the main user, and the calculation cost is high

Finally, although these methods can complete spectrum detection in a certain sense, they fail to complete the task of improving the throughput of cognitive users on the basis of optimizing the detection time, let alone improve the performance of cognitive wireless networks.

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