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Support Vector Machines for Direction of Arrival Estimation
 

Summary: Support Vector Machines for Direction
of Arrival Estimation
Judd A. Rohwer # and Chaouki T. Abdallah +
1 Abstract
Machine learning research has largely been devoted to binary and multiclass
problems relating to data mining, text categorization, and pattern/facial recog­
nition. 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 implemen­
tation of SVMs for DOA estimation and adaptive beamforming, an important
component of code division multiple access (CDMA) communication systems.
2 Introduction
Machine learning techniques have been applied to various problems relating to
cellular communications. In our research we present a machine learning based
approach for DOA estimation in a CDMA communication system [1]. The
DOA estimates are used in adaptive beamforming for interference suppression,

  

Source: Abdallah, Chaouki T- Electrical and Computer Engineering Department, University of New Mexico

 

Collections: Engineering