Deep learning cognitive radio network (CRN) multi-user cooperative spectrum sensing method

Abstract

The invention provides a deep learning cognitive radio network (CRN) multi-user cooperative spectrum sensing method, in which correlations of adjacent secondary users and adjacent frequency channels in spaces and spectrum domains are utilized, and fine granularity characteristic distinguishing is carried out on spectrum sensing data through a convolutional neural network (CNN) to improve the sensing precision. The method specifically comprises the following steps: a constructed multi-layer CNN model is firstly trained through the sensing data of the various users; current spectrum sensing results of the secondary users are then input into the trained CNN model; and the model automatically extracts characteristics of the sensing data, and carries out classification and identification on theextracted characteristics, so that cooperative spectrum sensing results of the secondary users for a current frequency channel of a primary user are obtained.

Description

technical field [0001] The invention belongs to the field of cognitive wireless networks, and in particular relates to a cognitive wireless network multi-user cooperative spectrum sensing method based on deep learning. Background technique [0002] The rapid development of science and technology has driven the continuous progress and improvement of communication technology. Driven by emerging wireless communication technologies, the scale of my country's wireless mobile network has continued to expand, the number of mobile terminal users has increased rapidly, and wireless communication business applications have flourished. While satisfying people's needs for communication services, it also brings about a serious problem of scarcity and shortage of spectrum resources. Therefore, improving the spectral efficiency of wireless communication systems and actively adapting to complex network electromagnetic environments has always been a research hotspot and difficulty in the fie...

AI Technical Summary

Problems solved by technology

The main challenge for CRN spectrum sensing is to degrade its performance due to the time-varying nature of channel fading, noise and interference

Although various methods such as energy detection, matched filtering, cyclostationary detection, intelligent sensing, and cooperative spectrum sensing have been proposed, with the diversity of services and access scenarios, complex heterogeneous cognitive wireless Sensing technology puts forward higher requirements

However, the existing technologies have room for improvement in terms of performance and complexity. Therefore, it is necessary to combine the development trend of the CRN network, especially its intelligent evolution trend, to study a new method of spectrum sensing based on machine learning to improve the performance of the CRN network.

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