Maximum margin equalizers trained with the Adatron algorithm
Signal Processing
Support vector machines framework for linear signal processing
Signal Processing
Adaptive minimum error-rate filtering design: A review
Signal Processing
Design methodology for configurable analog to digital conversion using support vector machines
Microelectronics Journal
Noise reduction and edge detection via kernel anisotropic diffusion
Pattern Recognition Letters
The method for solving two types of errors in customer segmentation on unbalanced data
Proceedings of the 10th international conference on Electronic commerce
Journal of Intelligent and Robotic Systems
Adaptive constrained learning in reproducing Kernel Hilbert spaces: the robust beamforming case
IEEE Transactions on Signal Processing
Fuzzy compensation support vector classification for direction of arrival estimation
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Low-complexity equalization based on least squares support vector classifiers for DS-UWB systems
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Harmonic source model based on support vector machine
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Learning control for space robotic operation using support vector machines
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Nonlinear channel equalization using concurrent support vector machine processor
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
A location based text mining method using ANN for geospatial KDD process
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
A sparse kernel algorithm for online time series data prediction
Expert Systems with Applications: An International Journal
Adaptive SVM-Based classification systems based on the improved endocrine-based PSO algorithm
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Hi-index | 35.69 |
The emerging machine learning technique called support vector machines is proposed as a method for performing nonlinear equalization in communication systems. The support vector machine has the advantage that a smaller number of parameters for the model can be identified in a manner that does not require the extent of prior information or heuristic assumptions that some previous techniques require. Furthermore, the optimization method of a support vector machine is quadratic programming, which is a well-studied and understood mathematical programming technique. Support vector machine simulations are carried out on nonlinear problems previously studied by other researchers using neural networks. This allows initial comparison against other techniques to determine the feasibility of using the proposed method for nonlinear detection. Results show that support vector machines perform as well as neural networks on the nonlinear problems investigated. A method is then proposed to introduce decision feedback processing to support vector machines to address the fact that intersymbol interference (ISI) data generates input vectors having temporal correlation, whereas a standard support vector machine assumes independent input vectors. Presenting the problem from the viewpoint of the pattern space illustrates the utility of a bank of support vector machines. This approach yields a nonlinear processing method that is somewhat different than the nonlinear decision feedback method whereby the linear feedback filter of the decision feedback equalizer is replaced by a Volterra filter. A simulation using a linear system shows that the proposed method performs equally to a conventional decision feedback equalizer for this problem