[1]冯径,熊鑫立,蒋磊.软件通信适配器的调制模式识别算法[J].东南大学学报(自然科学版),2017,47(3):456-460.[doi:10.3969/j.issn.1001-0505.2017.03.007]
 Feng Jing,Xiong Xinli,Jiang Lei.Modulation classification algorithm for software-designed communication adapter[J].Journal of Southeast University (Natural Science Edition),2017,47(3):456-460.[doi:10.3969/j.issn.1001-0505.2017.03.007]
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软件通信适配器的调制模式识别算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第3期
页码:
456-460
栏目:
计算机科学与工程
出版日期:
2017-05-20

文章信息/Info

Title:
Modulation classification algorithm for software-designed communication adapter
作者:
冯径1熊鑫立2蒋磊1
1解放军理工大学气象海洋学院, 南京 211101; 2解放军理工大学指挥信息系统学院, 南京 210007
Author(s):
Feng Jing1 Xiong Xinli2 Jiang Lei1
1Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China
2Institute of Command Information System, PLA University of Science and Technology, Nanjing 210007, China
关键词:
异构卫星网络 软件通信适配器 自动调制模式识别 高阶累积量 信息熵
Keywords:
heterogeneous satellite network software-designed communication adapter automatic modulation classification high-order cumulants information entropy
分类号:
TP393
DOI:
10.3969/j.issn.1001-0505.2017.03.007
摘要:
在异构卫星网络动态组网时,为了解决星上软件通信适配器对物理层调制模式识别率低的问题,提出了一种适合低信噪比和贫先验知识的自动调制模式识别算法.该算法以高斯白噪声信道作为信道模型,选取信号高阶累积量和经典统计量作为特征参数,采用引力搜索算法对径向基神经网络基函数中心进行优化,并在引力搜索算法中引入粒子群的信息熵来调节算法执行过程中探索与开采的关系,进一步提高了算法的分类和泛化能力.然后,利用仿真试验测评了该算法对6种卫星常用调相调制信号的识别效果.仿真试验结果表明,没有先验知识的情况下,该算法在调制信号信噪比大于4 dB时就可以达到100%的识别率,从而证明了该算法在低信噪比和贫先验知识条件下的有效性,说明算法满足星上软件通信适配器对物理层调制模式的识别要求.
Abstract:
To overcome the low recognition rate of the SDCA(software-designed communication adapter)on the satellite for the physic layer modulation during heterogeneous satellite dynamic networking, a novel AMC(automatic modulation classification)algorithm is proposed for low SNR(signal noise ratio)and poor previous knowledge scenario. In this algorithm, the AWGN(additive white Gaussian noise)is used as the channel model. The high-order cumulants and the classical statistics are selected as the features. The classification and generalization capability of the algorithm is enhanced by the IEGSA(information entropy improved gravitational search algorithm)to optimize the basis function center of the RBFNN(radical basis function neural network), using the information entropy of agents to balance exploration and exploitation in iteration. Then, the recognition effects of the proposed algorithm on six kinds of satellite phase modulation signals are evaluated by simulation. The experimental results show that without previous knowledge of received signals, the recognition rate of the proposed algorithm can achieve 100% when SNR is above 4 dB, proving the effectiveness of the algorithm under the condition with low SNR and poor priori knowledge. This algorithm can meet the requirements of the SDCA to the classify modulation mode.

参考文献/References:

[1] Dobre O A, Abdi A, Bar-Ness Y, et al. Survey of automatic modulation classification techniques: Classical approaches and new trends[J]. IET Communications, 2007, 1(2):137-156. DOI:10.1049/iet-com:20050176.
[2] 国北辰.自适应卫星通信调制方式决策与识别技术研究[D].哈尔滨:哈尔滨工业大学电子与信息工程学院,2013.
[3] 李剑, 江成顺, 侯毅刚. 基于优化RBF神经网络的集成算法及其在调制识别中的应用[J]. 信息工程大学学报, 2010, 11(4):448-451. DOI:10.3969/j.issn.1671-0673.2010.04.015.
Li Jian, Jiang Chengshun, Hou Yigang. Application of improved RBF neural network ensemble algorithm to modulation recognition[J]. Journal of Information Engineering University, 2010, 11(4):448-451. DOI:10.3969/j.issn.1671-0673.2010.04.015. (in Chinese)
[4] Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: A gravitational search algorithm[J]. Information Sciences, 2009, 179(13):2232-2248. DOI:10.1016/j.ins.2009.03.004.
[5] Sobolewski S, Adams W L, Sankar R. Universal nonhierarchical automatic modulation recognition techniques for distinguishing bandpass modulated waveforms based on signal statistics, cumulant, cyclostationary, multifractal and fourier-wavelet transforms features[C]//IEEE Military Communications Conference. Baltimore, MD, USA, 2014:748-753. DOI:10.1109/milcom.2014.130.
[6] 李晏.基带PSK、QAM信号调制子类自动识别研究[D].成都:电子科技大学电子工程学院,2008.
[7] Weber C, Felhauer T, Peter M. Automatic modulation classification technique for radio monitoring[J]. Electronics Letters, 2015, 51(10):794-796. DOI:10.1049/el.2015.0610.
[8] Tsakmalis A, Chatzinotas S, Ottersten B. Automatic modulation classification for adaptive power control in cognitive satellite communications[C]//2014 7th Advanced Satellite Multimedia Systems Conference and the 13th Signal Processing for Space Communications Workshop(ASMS/SPSC). Livorno, Italy, 2014:234-240.
[9] Li C, Xiao J, Xu Q. A novel modulation classification for PSK and QAM signals in wireless communication[C]//IET International Conference on Communication Technology and Application(ICCTA 2011). Beijing, China, 2011:89-92.
[10] Aslam M W, Zhu Z, Nandi A K. Automatic digital modulation classification using genetic programming with K-nearest neighbor[C]//2010 MILCOM Military Communications Conference. San Jose, CA, USA, 2010:1731-1736. DOI:10.1109/milcom.2010.5680232.

备注/Memo

备注/Memo:
收稿日期: 2016-10-12.
作者简介: 冯径(1963—),女,博士,教授,博士生导师,fengjing863@gmail.com.
基金项目: 国家自然科学基金资助项目(61371119).
引用本文: 冯径,熊鑫立,蒋磊.软件通信适配器的调制模式识别算法[J].东南大学学报(自然科学版),2017,47(3):456-460. DOI:10.3969/j.issn.1001-0505.2017.03.007.
更新日期/Last Update: 2017-05-20