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Cooperative automatic modulation recognition in cognitive radio

ISSN号：1006-8885
期刊名称：The Journal of China Universities of Posts and Tel
时间：0
页码：46-52
语言：英文
分类：TN929.5[电子电信—通信与信息系统;电子电信—信息与通信工程] TN92[电子电信—通信与信息系统;电子电信—信息与通信工程]
作者机构：[1]Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China, [2]National Mobile Communications Research Laboratory, Southeast University, Nanjing 210003, China
相关基金：This work was supported by the National Natural Science Foundation of China （60772062）, the National Basic Research Program of China （2007CB310607）, National Science and Technology Key Project （2009ZX03003-002）, the Open Research Fund of National Mobile Communications Research Laboratory and Southeast University （N200813）.
相关项目：认知无线电机会频谱接入关键技术研究

作者： ZHU Qi|CHEN Mei|

关键词：自动调制识别, 认知无线电, 合作社, 反向传播学习算法, BP神经网络, 加性白高斯噪声, 无线电接收器, 识别性能, cooperative modulation recognition, cognitive radio （CR）, feature parameter, neural network

中文摘要：

在这篇文章，合作调整识别(CMR ) 的一个新有效方法被建议为认知收音机接收装置认出主要用户的不同调整类型。在认知收音机(CR ) 系统，二个 CR 用户分别地把他们的特征参数送到合作识别中心，它背繁殖是镇静的神经网络(BPNN ) 。与二个用户的合作和与动量学习算法的错误背繁殖的应用程序，中心改进调整识别的性能，特别当 CR 用户的 signal-to-noise 比率(SNR ) 之一是低的时。测量建议方法的表演，模拟被执行分类添加剂白人 Gaussian 噪音(AWGN ) 贿赂的调制信号的不同类型。模拟结果证明没有合作，这个合作算法比那些有更好的识别表演。

英文摘要：

In this article, a new effective method of cooperative modulation recognition （CMR） is proposed to recognize different modulation types of primary user for cognitive radio receivers. In the cognitive radio （CR） system, two CR users respectively send their feature parameters to the cooperative recognition center, which is composed of back propagation neural network （BPNN）. With two users＇ cooperation and the application of an error back propagation learning algorithm with momentum, the center improves the performance of modulation recognition, especially when one of the CR users＇ signal-to-noise ratio （SNR） is low. To measure the performance of the proposed method, simulations are carried out to classify different types of modulated signals corrupted by additive white Gaussian noise （AWGN）. The simulation results show that this cooperation algorithm has a better recognition performance than those without cooperation.

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