Electrical & Computer Engineering Faculty Publications

Document Type

Article

Publication Date

4-26-2012

Abstract

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.

Language (ISO)

English

Sponsorship

Sandia Labs; The University of New Mexico

Comments

Pre-Print

Download

Share

COinS