Signal Detection by Using M-ary Support Vector Machine for 16-QAM Coherent Optical Systems with Nonlinear Phase Noise

2013 ◽  
Author(s):  
Minliang Li ◽  
Song Yu ◽  
Zhixiao Chen ◽  
Jie Yang ◽  
Yi Han ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Signal Detection ◽  
Phase Noise ◽  
Optical Systems ◽  
Support Vector ◽  
Nonlinear Phase

scholarly journals Nonparameter Nonlinear Phase Noise Mitigation by Using M-ary Support Vector Machine for Coherent Optical Systems

2013 ◽  
Vol 5 (6) ◽  
pp. 7800312-7800312 ◽  
Author(s):  
Minliang Li ◽  
Song Yu ◽  
Jie Yang ◽  
Zhixiao Chen ◽  
Yi Han ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Phase Noise ◽  
Optical Systems ◽  
Support Vector ◽  
Noise Mitigation ◽  
Nonlinear Phase

scholarly journals Mitigation of Nonlinear Impairments by Using Support Vector Machine and Nonlinear Volterra Equalizer

2019 ◽  
Vol 9 (18) ◽  
pp. 3800
Author(s):  
Rebekka Weixer ◽  
Jonas Koch ◽  
Patrick Plany ◽  
Simon Ohlendorf ◽  
Stephan Pachnicke
Keyword(s):  
Support Vector Machine ◽  
Phase Noise ◽  
Nonlinear Effects ◽  
Single Mode ◽  
Single Mode Fiber ◽  
Kernel Functions ◽  
Training Data ◽  
Support Vector ◽  
Data Set ◽  
Nonlinear Phase

A support vector machine (SVM) based detection is applied to different equalization schemes for a data center interconnect link using coherent 64 GBd 64-QAM over 100 km standard single mode fiber (SSMF). Without any prior knowledge or heuristic assumptions, the SVM is able to learn and capture the transmission characteristics from only a short training data set. We show that, with the use of suitable kernel functions, the SVM can create nonlinear decision thresholds and reduce the errors caused by nonlinear phase noise (NLPN), laser phase noise, I/Q imbalances and so forth. In order to apply the SVM to 64-QAM we introduce a binary coding SVM, which provides a binary multiclass classification with reduced complexity. We investigate the performance of this SVM and show how it can improve the bit-error rate (BER) of the entire system. After 100 km the fiber-induced nonlinear penalty is reduced by 2 dB at a BER of 3.7 × 10 − 3 . Furthermore, we apply a nonlinear Volterra equalizer (NLVE), which is based on the nonlinear Volterra theory, as another method for mitigating nonlinear effects. The combination of SVM and NLVE reduces the large computational complexity of the NLVE and allows more accurate compensation of nonlinear transmission impairments.

Research on Fault Diagnosis of PCCP Based on Support Vector Machine

2010 ◽  
Vol 108-111 ◽  
pp. 409-414
Author(s):  
Chun Ting Yang ◽  
Yang Liu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Signal Detection ◽  
Acoustic Signal ◽  
Kernel Functions ◽  
Support Vector ◽  
Catastrophic Failure ◽  
Training Samples ◽  
The World ◽  
Good Classification

Recent years, many utilities have experienced catastrophic rupture of critical Prestressing Concrete Cylinder Pipe (PCCP) lines throughout the world. Much attention has been focused on reliably assessing the condition of PCCP mains. However, assessment of embedded prestressing wire is difficult. Continuous acoustic monitoring can provide a means of locating problematic pipe sections. In this paper the application of support vector machine (SVM) in acoustic signal detection is proposed. And the effect of this method is investigated. Some key parameters of SVM and kernel functions are surveyed. SVM methods are more effective, especially for the case of lack of training samples. The experiment shows that the SVM method has good classification ability and robust performances. The techniques can provide the opportunity to identify problematic pipe sections and repair the pipe prior to failure. Therefore it can help to prolong the life of a suspect pipeline while minimizing the potential for catastrophic failure.

Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning

2016 ◽  
Vol 369 ◽  
pp. 199-208 ◽  
Author(s):  
Danshi Wang ◽  
Min Zhang ◽  
Zhongle Cai ◽  
Yue Cui ◽  
Ze Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Phase Noise ◽  
Optical Systems ◽  
Nonlinear Phase

Automatic signal detection based on support vector machine

2007 ◽  
Vol 20 (1) ◽  
pp. 88-97 ◽  
Author(s):  
Hai-jun Wang ◽  
Gui-zhong Liu
Keyword(s):  
Support Vector Machine ◽  
Signal Detection ◽  
Support Vector

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector
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