机器学习在无线通信中的应用研究

Research on Application of Machine Learning in Wireless Communication

机器学习属于人工智能类科学,其涵盖了数学、自动化及计算机科学等诸多学科,是人工智能技术的关键所在。主要探讨了怎样对所测数据进行分析并获取其中规律,然后以得到的规律来预测不确定或者难以观测到的数据。因此,首先阐述有关的机器学习内容,其次说明在进一步学习下的降峰值平均功率比(Peak to Average Power Ratio,PAPR)方法、支持向量机(Support Vector Machines,SVM)下的场景类别划分办法、在线资源配置办法、基于深度学习的信道编译码以及基于深度学习的物理层端到传输端的机器学习在无线通信中的应用模式。

Machine learning belongs to the science of artificial intelligence,which covers many disciplines such as mathematics,automation,and computer science,and is the key to artificial intelligence technology.This article mainly discusses how to analyze the measured data and obtain the laws therein,and then use the obtained laws to predict uncertain or difficult to observe data.Therefore,first,the relevant machine learning content will be described,followed by the Peak to Average Power Ratio(PAPR)method under further learning,the scenario classification method under Support Vector Machines(SVM),and the online resource allocation method,the application mode of channel encoding and decoding based on deep learning and physical layer end-to-end machine learning based on deep learning in wireless communication.