王欣,
陈思吉,
崔太平
重庆邮电大学通信与信息工程学院 重庆 400065
基金项目:国家自然科学基金(61571073)
详细信息
作者简介:申滨：男，1978年生，教授，研究方向为大规模MIMO系统、认知无线电等
王欣：女，1992年生，硕士生，研究方向为认知无线电
陈思吉：男，1993年生，硕士生，研究方向为认知无线电
崔太平：男，1981年生，讲师，研究方向为认知无线电、车联网等
通讯作者:申滨 shenbin@cqupt.edu.cn
1) 在CCRN中，由于SUE与其周围的多个蜂窝基站之间的无线链接，假设SUE的位置信息能够通过相应的定位方法较为精确地获得。中图分类号:TN911
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出版历程
收稿日期:2019-12-19
修回日期:2020-03-17
网络出版日期:2020-09-16
刊出日期:2021-01-15
Machine Learning Based Primary User Transmit Mode Classification for Spectrum Sensing in Cellular Cognitive Radio Network
Bin SHEN,,Xin WANG,
Siji CHEN,
Taiping CUI
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Funds:The National Nature Science Foundation of China (61571073)
摘要
摘要:近年来，基于机器学习(ML)的频谱感知技术为认知无线电系统提供了新型的频谱状态监测解决方案。利用蜂窝认知无线电网络(CCRN)中的次级用户设备(SUE)所能提供的大量频谱观测数据，该文提出了一种基于主用户(PU)传输模式分类的频谱感知方案。首先，基于多种典型的ML算法，对于网络中的多个主用户发射机(PUT)的传输模式进行分类辨识，在网络整体层面上确定所有PUT的联合工作状态。然后，网络中的SUE根据其所处地理位置或者频谱观测数据，判断其在当前已判定的PUT发射模式下接入授权频谱的可能性。由于PUT在网络中的实际位置可能事先已知或者无法提前确定，该文给出了3种不同的处理方法。理论推导与实验结果表明，所提方案与传统的能量检测方案相比，不仅改善了频谱感知性能，还增加了蜂窝认知网络对于授权频谱的动态访问机会。该方案可以作为蜂窝认知无线电网络中的一种高效实用的频谱感知解决方案。
关键词:蜂窝认知无线电网络/
机器学习/
频谱感知/
支持向量机/
卷积神经网络
Abstract:In recent years, Machine Learning (ML) based spectrum sensing technology has provided a new solution in spectrum status identification for cognitive radio systems. Based on the large amount of spectrum observations captured by the Secondary User Equipment (SUE) in the Cellular Cognitive Radio Network (CCRN), this paper proposes a spectrum sensing scheme based on the Primary User (PU) transmission mode classification. Firstly, based on a variety of typical ML classification algorithms, the proposed scheme classifies the transmission mode of multiple Primary User Transmitters (PUTs) in the CCRN, and determines the joint operating state of all the PUTs in the CCRN. Subsequently, the SUE evaluates the possibility of accessing the licensed spectrum in the currently determined PUT transmission mode according to its geographical location or spectrum observation data. Since the actual locations of the PUTs in the network may be readily known in advance or unaware of at all, the proposed scheme solves the problem in three different methods. Theoretical derivation and experimental results show that compared with the traditional energy detection scheme, the proposed scheme not only remarkably improves the spectrum sensing performance, but also significantly increases the opportunities of dynamic accessing to the licensed spectrum for the SUEs. The proposed scheme can be used as an efficient and practical spectrum sensing solution in the CCRN.
Key words:Cellular Cognitive Radio Network (CCRN)/
Machine learning/
Spectrum sensing/
Support vector machine/
Convolutional neural network
注释:
1) 1) 在CCRN中，由于SUE与其周围的多个蜂窝基站之间的无线链接，假设SUE的位置信息能够通过相应的定位方法较为精确地获得。
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