Least squares support vector machines for direction of arrival estimation

Machine learning research has largely been devoted to binary and multiclass problems relating to data mining, text categorization, and pattern/facial recognition. Recently, popular machine learning algorithms, including support vector machines (SVM), have successfully been applied to wireless communication problems. The paper presents a multiclass least squares SVM (LS-SVM) architecture for direction of arrival (DOA) estimation as applied to a CDMA cellular system. Simulation results show a high degree of accuracy, as related to the DOA classes, and prove that the LS-SVM DDAG (decision directed acyclic graph) system has a wide range of performance capabilities. The multilabel capability for multiple DOAs is discussed. Multilabel classification is possible with the LS-SVM DDAG algorithm presented

Least squares support vector machines for direction of arrival estimation with error control and validation

The paper presents a multiclass, multilabel implementation of least squares support vector machines (LS-SVM) for direction of arrival (DOA) estimation in a CDMA system. For any estimation or classification system, the algorithm\u27s capabilities and performance must be evaluated. Specifically, for classification algorithms, a high confidence level must exist along with a technique to tag misclassifications automatically. The presented learning algorithm includes error control and validation steps for generating statistics on the multiclass evaluation path and the signal subspace dimension. The error statistics provide a confidence level for the classification accuracy

Machine learning based CDMA power control

This paper presents binary and multiclass machine learning techniques for CDMA power control. The power control commands are based on estimates of the signal and noise subspace eigenvalues and the signal subspace dimension. Results of two different sets of machine learning algorithms are presented. Binary machine learning algorithms generate fixed-step power control (FSPC) commands based on estimated eigenvalues and SIRs. A fixed-set of power control commands are generated with multiclass machine learning algorithms. The results show the limitations of a fixed-set power control system, but also show that a fixed-set system achieves comparable performance to high complexity closed-loop power control systems

Least squares support vector machines for fixed-step and fixed-set CDMA power control

This paper presents two machine learning based algorithms for CDMA power control. The least squares support vector machine (LS-SVM) algorithms classify eigenvalues estimates into sets of power control commands. A binary LS-SVM algorithm generates fixed step power control (FSPC) commands, while the one vs. one multiclass LS-SVM algorithm generates estimates for fixed set power control

Support Vector Machines for Direction of Arrival Estimation

Machine learning research has largely been devoted to binary and multiclass problems relating to data mining, text categorization, and pattern/facial recognition. Recently, popular machine learning algorithms have successfully been applied to wireless communication problems, notably spread spectrum receiver design, channel equalization, and adaptive beamforming with direction of arrival estimation (DOA). Various neural network algorithms have been widely applied to these three communication topics. New advanced learning techniques, such as support vector machine (SVM) have been applied, in the binary case, to receiver design and channel equalization. This paper presents a multiclass implementation of SVMs for DOA estimation and adaptive beamforming, an important component of code division multiple access (CDMA) communication systems.

Least squares support vector machines for direction of arrival estimation

Digital Object Identifier: 10.1109/APS.2003.1217400Machine learning research has largely been devoted to binary and multiclass problems relating to data mining, text categorization, and pattern/facial recognition. Recently, popular machine learning algorithms, including support vector machines (SVM), have successfully been applied to wireless communication problems. The paper presents a multiclass least squares SVM (LS-SVM) architecture for direction of arrival (DOA) estimation as applied to a CDMA cellular system. Simulation results show a high degree of accuracy, as related to the DOA classes, and prove that the LS-SVM DDAG (decision directed acyclic graph) system has a wide range of performance capabilities. The multilabel capability for multiple DOAs is discussed. Multilabel classification is possible with the LS-SVM DDAG algorithm presented.IEE