一种基于信号特征分析和机器学习技术的900 MHz频段干扰自动定位方法

A 900 MHz Frequency Band Interference Automatic Localization Method Based on Signal Feature Analysis and Machine Learning Technology

随着无线电通信的迅速发展,频谱资源日益紧张,各种干扰问题也越来越严重。其中,900 M Hz频段的干扰问题尤为突出。该文提出了一种基于信号特征分析和机器学习技术的900 MHz频段干扰自动定位方法,通过对干扰信号的频谱、时域、调制等特征进行分析,建立干扰信号特征库,并利用机器学习算法对信号进行分类和定位。实验结果表明,该文提出的方法具有较高的干扰定位准确率和稳定性,能够有效地提高频谱资源利用效率的闭环。

With the rapid development of wireless communication,spectrum resources are becoming increasingly scarce,and various interference problems are becoming increasingly serious.Among them,the interference problem in the 900 MHz frequency band is particularly prominent.This article proposes a 900 MHz frequency band interference automatic localization method based on signal feature analysis and machine learning technology.By analyzing the spectrum,time domain,modulation and other characteristics of interference signals,a feature library of interference signals is established,and machine learning algorithms are used to classify and locate signals.The experimental results show that the method proposed in this paper has high interference localization accuracy and stability,and can effectively improve the closed-loop efficiency of spectrum resource utilization.