Dynamic arrival spectrum sensing method based on absolute value accumulation

Abstract

The invention provides a dynamic arrival spectrum sensing method based on absolute value accumulation. The method is mainly divided into two steps: a signal detection process and a signal decision process; and the method specifically includes the step that: a secondary user first takes a dynamic arrival signal of a Poisson process as a received signal, and then performs absolute value processing on the received signal, takes a cumulative summation result as the decision statistic, and further judges whether a primary user exists or not to achieve spectrum sensing. According to the scheme of the invention, in the case of low signal-to-noise ratios, the problem of 'spike' of Laplace noises can also be improved, the detection performance of an algorithm can be improved, and thus the effectiveutilization of spectrum can be achieved.

Description

technical field [0001] The invention belongs to the field of wireless communication, and relates to a wireless communication system cognitive radio spectrum sensing technology. Background technique [0002] With the continuous growth of wireless services, the demand for spectrum continues to increase, and the popularity of wireless devices (smart phones, tablet computers, etc.), the current fixed spectrum allocation model cannot provide additional available spectrum for high-speed data transmission wireless communication systems Bandwidth, and the actual survey data show that many spectrum resources that have been allocated are still underutilized. The research results of the Spectrum Policy Task Force of the US FCC (Federal Communications Commission) show that in the United States, the actual utilization rate of the licensed spectrum is only between 15% and 85%, and the idle rate of the licensed spectrum is high. The emergence of cognitive radio (Cognitive Radio) is to mak...

AI Technical Summary

Problems solved by technology

[0012] (1) Most of the existing spectrum sensing algorithms are based on the ideal Gaussian noise environment

Due to the existence of a large number of wireless signals and various complex interferences in the environment of the actual radio receiving equipment, the receiver works in a non-Gaussian noise environment

However, this type of noise contains large-scale spikes. It is unreasonable to use Gaussian noise to describe this type of noise.

In the background of non-Gaussian noise, if the spectrum sensing algorithm of Gaussian noise is used directly, the detection performance of the algorithm will be reduced

[0013] (2) The methods mentioned above all assume that the state of the primary user remains unchanged in the sensing cycle. This assumption is difficult to realize in practice. In order to detect the primary user under low SNR, spectrum sensing requires It takes a long time to collect sampling information. During the signal sensing period, the state of the primary user may change. For example, the primary user may arrive in an arbitrary sensing period, and the secondary user may not detect the primary user by using the above spectrum sensing method. The state of the user, therefore, the secondary user may send information in the primary user frequency band, and the spectrum occupied by the secondary user in the primary user frequency band will cause interference to the primary user. This situation should be strictly avoided in cognitive radio spectrum sensing

[0014] Therefore, the traditional spectrum sensing algorithm must be improved according to the actual dynamic environment when it is applied to the spectrum sensing system

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