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Blind Source SeparationDependent Component Analysis Using Precoding

Blind Source Separation: Dependent Component Analysis Using Precoding [Most existing methods for dependent component analysis only work under the condition that the source signals are nonnegative and/or sparse [1, 2]. Unfortunately, the signals in many real-world applications such as wireless communication systems are neither nonnegative nor sparse. In this chapter, three precoding based methods are presented to separate nonnegative and non-sparse but spatially correlated sources. The precoding based methods take advantage of the fact that in some applications, the source signals are accessible before being mixed up. For example, in a wireless communication system, the user signals at the transmission end are accessible prior to being transmitted to the receiver. This provides an opportunity to preprocess them before transmission such that BSS can be achieved at the receiver. Different from the method in [3], the precoding based methods do not impose any condition on the time-frequencys distributions of the sources.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Blind Source SeparationDependent Component Analysis Using Precoding

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Publisher
Springer Singapore
Copyright
© The Author(s) 2015
ISBN
978-981-287-226-5
Pages
73 –90
DOI
10.1007/978-981-287-227-2_4
Publisher site
See Chapter on Publisher Site

Abstract

[Most existing methods for dependent component analysis only work under the condition that the source signals are nonnegative and/or sparse [1, 2]. Unfortunately, the signals in many real-world applications such as wireless communication systems are neither nonnegative nor sparse. In this chapter, three precoding based methods are presented to separate nonnegative and non-sparse but spatially correlated sources. The precoding based methods take advantage of the fact that in some applications, the source signals are accessible before being mixed up. For example, in a wireless communication system, the user signals at the transmission end are accessible prior to being transmitted to the receiver. This provides an opportunity to preprocess them before transmission such that BSS can be achieved at the receiver. Different from the method in [3], the precoding based methods do not impose any condition on the time-frequencys distributions of the sources.]

Published: Sep 17, 2014

Keywords: Precoding; Mutually correlated sources; Dependent component analysis; Singular value; Singular vector

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