Demodulation of a M-Ray Position Phase Shift Keying System Using Multi-Class Support Vector Machine Classification

2014 ◽  
Vol 687-691 ◽  
pp. 3840-3843 ◽  
Author(s):  
Xian Qing Chen ◽  
Shu Bo Song ◽  
Wu Zhou
Keyword(s):  
Support Vector Machine ◽  
Phase Shift ◽  
Back Propagation ◽  
Symbol Error Rate ◽  
Support Vector ◽  
Phase Shift Keying ◽  
New Approach ◽  
Svm Classification ◽  
Shift Keying ◽  
Better Than

In this paper, we introduce a new approach for nonlinear demodulation based on multi-class support vector machine (SVM) classification. We propose to measure the performance of this demodulator with different M which is the parameter of M-ray position phase shift keying (MPPSK) modulation, and compare with other demodulation technique. During demodulation, a few sampling points are chosen for multi-class SVM training and testing, which can reduce the complexity of system. Simulation results show that this new approach significantly outperforms the method of using Phase Locked Loop (PLL) demodulation by 10dB, and also better than Back Propagation Artificial Neural Networks (ANN-BP) classification demodulation. With the growth of M, the data rate increased and the performance become a little worse, but less bit SNR is used to achieve the same Symbol Error Rate (SER) as small M. So, it is an effective method to get better performance by using multi-class SVM classification technique for demodulation in MPPSK system.

scholarly journals Calculation of symbol error rate in a telecommunication system based on amplitude modulation of many components

2021 ◽  
Vol 8 (2) ◽  
pp. 137-149
Author(s):  
I. V. Horbatyi ◽  
Keyword(s):  
Phase Shift ◽  
Error Rate ◽  
Amplitude Modulation ◽  
Additive White Gaussian Noise ◽  
Symbol Error Rate ◽  
Communication Line ◽  
Phase Shift Keying ◽  
Telecommunication System ◽  
Shift Keying ◽  
Arbitrary Location

The known analytical equations for calculating the symbol error rate (SER) in the M-ary telecommunication system are considered. The analytical equations for calculating SER in a telecommunication system based on M-ary amplitude modulation of many components (M-AMMC) and other varieties of amplitude-phase shift keying with arbitrary number and arbitrary location of signal points of the signal constellation, as well as under the action of additive white Gaussian noise in a communication line are proposed. According to the results of the research, it is found that the proposed equations allow us to increase the accuracy of calculating SER when using M-AMMC and other varieties of amplitude-phase shift keying compared to known analytical equations.

scholarly journals INS/WSN-Integrated Navigation Utilizing LS-SVM andH∞Filtering

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Yuan Xu ◽  
Xiyuan Chen ◽  
Qinghua Li
Keyword(s):  
Support Vector Machine ◽  
Kalman Filter ◽  
Back Propagation ◽  
Sampling Period ◽  
Integrated System ◽  
Support Vector ◽  
Integrated Navigation ◽  
New Approach ◽  
Stochastic Uncertainty ◽  
Integrated Navigation System

In order to achieve continuous navigation capability in areas such as tunnels, urban canyons, and indoors a new approach using least squares support vector machine (LS-SVM) andH∞filter (HF) for integration of INS/WSN is proposed. In the integrated system, HF estimates the errors of position and velocity while the signals in WSNs are available. Meanwhile, the compensation model is trained by LS-SVM with corresponding HF states. Once outages of the signals in WSNs, the model is used to correct INS solution as HF does. Moreover, due to device reasons, there are slight fluctuations in sampling period in practice. For overcoming this problem of integrated navigation, the theoretical analysis and implementation of HF for an integrated navigation system with stochastic uncertainty are also given. Simulation shows the performance of HF is more robust compared with INS-only solution and Kalman filter (KF) solution, and the prediction of LS-SVM has the smallest error compared with INS-only and back propagation (BP), the improvement is particularly obvious.

Performance Comparison between DPSK and OQPSK modulation approaches in multi environments channels with Matlab Simulink models

2019 ◽  
Vol 7 (1) ◽  
pp. 30-39
Author(s):  
Fatima faydhe Al- Azzawi ◽  
Faeza Abas Abid ◽  
Zainab faydhe Al-Azzawi
Keyword(s):  
Phase Shift ◽  
Fading Channels ◽  
Additive White Gaussian Noise ◽  
Block Codes ◽  
Performance Comparison ◽  
Phase Shift Keying ◽  
Differential Phase Shift ◽  
Matlab Simulink ◽  
Communication Industry ◽  
Shift Keying

Phase shift keying modulation approaches are widely used in the communication industry. Differential phase shift keying (DPSK) and Offset Quadrature phase shift keying (OQPSK) schemes are chosen to be investigated is multi environment channels, where both systems are designed using MATLAB Simulink and tested. Cross talk and unity of signals generated from DPSK and OQPSK are examined using Cross-correlation and auto-correlation, respectively. In this research a proposed system included improvement in bit error rate (BER) of both systems in  the additive white Gaussian Noise (AWGN) channel, by using the convolutional and block codes, by increasing the ratio of energy in the specular component to the energy in the diffuse component (k) and  the diversity order BER in the fading channels will be improved in both systems.    

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

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