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Adaptive Modulation and Power Control for Throughput Enhancement in Cognitive Radios

期刊名称：Journal of Electronics(China)
时间：0
页码：65-69
语言：英文
分类：TN911.3[电子电信—通信与信息系统;电子电信—信息与通信工程] O235[理学—运筹学与控制论;理学—数学]
作者机构：[1]Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China Key Laboratory on Wideband Wireless Communications and Sensor Network Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
相关基金：Acknowledgements This work was supported by the National Basic Research Program of China （2007CB310607）, the National Natural Science Foundation of China （60772062）, and the National Science and Technology Key Project （2011ZX03001-006-02, 2011ZX03005-004-03）.
相关项目：认知无线电机会频谱接入关键技术研究

关键词：聚类有效性, 神经网络, 调制分类, 调制类型, 正交幅度调制, 调制识别, 学习算法, 分类方法, modulation classification, clustering, BP neural network, FCM

中文摘要：

在这份报纸，我们基于聚类和神经网络，一个新算法在被介绍提取特色的联合建议一个新调整分类方法。为了认出调整，基于象调音的阶段移动(PSK ) 和照振幅调整(正交调幅) 那样的星座图打字，聚类的模糊 C 工具(FCM ) 为在簇的不同数字下面恢复星座被采用。然后，簇有效性措施被使用提取在不同调整类型之间区别的特色。特征被送到神经网络以便调整类型能被认出。为了征服标准的劣势，支持繁殖(BP ) 神经网络，结合坡度听说 Polak-Ribiere 更改的算法被采用改进集中的速度和调整识别的表演。算法的分类率在这份报纸建议了的模拟结果表演比聚类算法的那些高得多。

英文摘要：

In this paper, we propose a new modulation classification method based on the combination of clustering and neural network, in which a new algorithm is introduced to extract key features. In order to recognize modulation types based on the constellation diagram such as phase shift keying （PSK） and quadrature amplitude modulation （QAM）, fuzzy C-means （FCM） clustering is adopted for recovering the constellation under different number of clusters. Then cluster validity measure is applied to extract key features which discriminate between different modulation types. The features are sent to neural network so that modulation types can be recognized. In order to conquer the disadvantages of standard back propagation （BP） neural network, conjugate gradient learning algorithm of Polak-Ribiere update is employed to improve the speed of convergence and the performance of modulation recognition. Simulation results show that classification rates of the algorithm proposed in this paper are much higher than those of clustering algorithm.

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