基于机器学习的5G通信目标信号识别方法

5G Communication Target Signal Recognition Method Based on Machine Learning

以往使用的理论决策法和模式识别法因受到噪声影响,导致识别精准度较低,基于此提出了基于机器学习的5G通信目标信号识别方法研究。基于机器学习信号识别原理,分析并模态分解5G通信目标信号,剔除噪声干扰信号后提取特征,通过计算通信信号相似度判断信号传输路径,以此设计目标信号识别流程。由实验验证结果可知,该方法识别精准度最高为0.98,具有良好的识别效果。

In the past,the oretical decision-making methods and pattern recognition methods were affected by noise,resulting in low recognition accuracy.Based on this,a research on 5G communication target signal recognition methods based on machine learning was proposed.Based on the principle of machine learning signal recognition,the 5G communication target signal is analyzed,the 5G communication target signal is modally decomposed,and the characteristics are extracted after removing the noise interference signal.Calculate the similarity of the communication signal,determine the signal transmission path,and design the target signal identification process.The experimental verification results show that this method has a maximum recognition accuracy of 0.98,which has a good recognition effect.