[1]陈贤卿,吴乐南,靳一.基于SVM分类的EBPSK信号解调判决[J].东南大学学报(自然科学版),2011,41(4):672-677.[doi:10.3969/j.issn.1001-0505.2011.04.003]
 Chen Xianqing,Wu Lenan,Jin Yi.Demodulation of EBPSK signals based on SVM classification[J].Journal of Southeast University (Natural Science Edition),2011,41(4):672-677.[doi:10.3969/j.issn.1001-0505.2011.04.003]
点击复制

基于SVM分类的EBPSK信号解调判决()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
41
期数:
2011年第4期
页码:
672-677
栏目:
信息与通信工程
出版日期:
2011-07-20

文章信息/Info

Title:
Demodulation of EBPSK signals based on SVM classification
作者:
陈贤卿吴乐南靳一
(东南大学信息科学与工程学院,南京 210096)
Author(s):
Chen XianqingWu LenanJin Yi
(School of Information Science and Engineering, Southeast University, Nanjing 210096, china)
关键词:
SVM分类EBPSK中频解调误码率
Keywords:
support vector machine classification extended binary phase shift keying intermediate-frequency demodulation bit error rate
分类号:
TN911.25
DOI:
10.3969/j.issn.1001-0505.2011.04.003
摘要:
为了改善扩展的二元相移键控(EBPSK)系统在低信噪比下的误码率性能,针对解调端冲击滤波器输出信号的特点,引入支持向量机(SVM)分类方法.在滤波器输出的中频信号中选取少量采样点进行判决,仿真显示,可以得到较高的信噪比增益.误码率在10-4时,比积分判决方法获得1.8dB的信噪比增益.不同核函数产生不同的支持向量机算法,进而对线性和径向基核函数作了分析,同时,对不同的特征点提取以及不同的训练码元个数对判决结果的影响作了较为详细的分析.通过仿真发现,用少量特征点和训练码元便可以获得较好的性能,因此,在EBPSK系统中采用SVM分类判决法降低误码率是一种较有效的方式.
Abstract:
To improve the bit error rate(BER) performance of the extended binary phase shift keying (EBPSK) demodulation under low signal to noise ratio (SNR) and according to the signal characteristics of impact filter output, the support vector machine (SVM) classification is introduced. A few signal samples are selected from the intermediate-frequency output of the filter to make the judgment. Simulation reveals that a higher SNR gain is obtained by adopting the SVM classification. Compared with integral decision 1. 8dB higher SNR gain can be obtained when BER is 10-4. Different kernel functions have different support vector machine algorithm, and then the radial basis function kernel and linear functions are analyzed. The effect of different characteristic point abstraction and different numbers of training elements on discrimination results are analyzed in detail. It is found from the simulation that using only a few characteristic points and training elements can achieve better results. Therefore, it is an effective way adopting SVM classification in EBPSK system to reduce BER.

参考文献/References:

[1] 吴乐南.超窄带高速通信进展[J].自然科学进展,2007,17(11):1467-1473.
  Wu Lenan.The evolution of ultra-narrow band and high speed communications[J].Progress in Nature Science,2007,17(11):1467-1473.(in Chinese)
[2] 戚晨皓,陈国强,吴乐南.二阶锁相环的EBPSK信号解调分析[J].电子与信息学报,2009,31(2):418-421.
  Qi Chenhao,Chen Guoqiang,Wu Lenan.EBPSK demodulation analysis based on second-order phase locked loop [J].Journal of Electronics &Information Technology,2009,31(2):418-421.(in Chinese)
[3] Feng Man,Wu Lenan.Special non-linear filter and extension to Shannon’s channel capacity[J].Digital Signal Processing,2009,19(5):861-873.
[4] 朱仁祥,吴乐南.基于最低误码率准则及Volterra序列的几何特征均衡器[J].信号处理,2008,24(6):1027-1031.
  Zhu Renxiang,Wu Lenan.Geometric feature equalizers based on minimum bit error rate criterion and volterra Series[J].Signal Processing,2008,24(6):1027-1031.(in Chinese)
[5] 冯熳,高鹏,吴乐南.超窄带调制信号的特殊滤波分析与仿真[J].东南大学学报:自然科学版,2010,40(2):227-230.
  Feng Man,Gao Peng,Wu Lenan.Analysis and simulation of special filtering based on ultra narrow band modulated signal[J].Journal of Southeast University:Natural Science Edition,2010,40(2):227-230.(in Chinese)
[6] Wu Lenan,Feng Man.On BER performance of EBPSK-MODEM in AWGN channel[J].Sensors,2010,10(4):3824-3834.
[7] Olmos P M,Murillo F J J,Perez C F.Joint nonlinear channel equalization and soft LDPC decoding with gaussian processes[J].IEEE Transactions on Signal Processing,2010,58(3):1183-1192.
[8] Ozertem U,Erdogmus D.RKHS bayes discriminant:a subspace constrained nonlinear feature projection for signal detection[J].IEEE Transactions on Neural Neworks,2009,20(7):1195-1203.
[9] Johnny W H K,Stevan M B,Vojislav K.Multiuser detector for chaos-based CDMA using support vector machine[J].IEEE Transactions on Neural Neworks,2010,21(8):1221-1231.
[10] Johnny W H K,Stevan M Berber.Demodulation of a chaos-based CDMA system using support vector machine[C]//IEEE ISWPC 2008.Santorini,Greece,2008:69-72.
[11] Cortes C,Vapnik V.Support vector networks[J].Machine Learning,1995,20(3):273-297.
[12] Keerthi S S,Lin C J.Asymptotic behaviors of supportvector machines with Gaussian kernel[J].Neuml Computation,2003,15(7):1667-1689.

相似文献/References:

[1]靳一,王继武,吴乐南.混合蛙跳算法优化的支持向量机EBPSK检测器[J].东南大学学报(自然科学版),2011,41(3):509.[doi:10.3969/j.issn.1001-0505.2011.03.015]
 Jin Yi,Wang Jiwu,Wu Lenan.EBPSK demodulator based on shuffled frog leaping optimized SVM[J].Journal of Southeast University (Natural Science Edition),2011,41(4):509.[doi:10.3969/j.issn.1001-0505.2011.03.015]

备注/Memo

备注/Memo:
作者简介:陈贤卿(1983—),男,博士生;吴乐南(联系人),男,博士,教授,博士生导师,wuln@seu.edu.cn.
基金项目:国家高技术研究发展计划(863计划)资助项目(2008AA01Z227)、国家自然科学基金资助项目(60872075).
引文格式: 陈贤卿,吴乐南,靳一.基于SVM分类的EBPSK信号解调判决[J].东南大学学报:自然科学版,2011,41(4):672-677.[doi:10.3969/j.issn.1001-0505.2011.04.003]
更新日期/Last Update: 2011-07-20