FebICA: Feedback Independent Component Analysis for Complex Domain Source Separation of Communication Signals
In this chapter, an effective blind source separation (BSS) algorithm is applied to solve the co-channel interference problem in wireless communication systems. Algorithms developed for this purpose must not only have the capability of working in the comp
- PDF / 1,137,946 Bytes
- 21 Pages / 439.37 x 666.142 pts Page_size
- 55 Downloads / 191 Views
FebICA: Feedback Independent Component Analysis for Complex Domain Source Separation of Communication Signals A. K. Kattepur and F. Sattar
Abstract In this chapter, an effective blind source separation (BSS) algorithm is applied to solve the co-channel interference problem in wireless communication systems. Algorithms developed for this purpose must not only have the capability of working in the complex domain and improving output signal to interference plus noise ratio (SINR), but also have relatively low computational complexity. We propose a fast Fourier transform (FFT)-based algorithm called feedback independent component analysis (FebICA) that is able to blindly separate complex modulated digital signals. By applying this algorithm to communication signals, it is observed that it has the advantages of SINR gain improvement as well as low computational complexity. The performance of the FebICA algorithm is shown to be better than the joint approximate diagonalization of eigen-matrices (JADE) algorithm in terms of the output SINR and requires lower computational complexity than the analytical constant modulus algorithm (ACMA). The algorithm is also shown to be more robust with increasing number of sources compared to other algorithms. The separation performance by using the collected field data has also been demonstrated.
18.1 Introduction Blind source separation (BSS) algorithms are used for separating individual sources from their mixtures with minimal a priori information about the source signals or
A. K. Kattepur (B) Inria Paris - Rocquencourt, Domaine de Voluceau, Le Chesnay, France e-mail: [email protected] F. Sattar Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada e-mail: [email protected] G. R. Naik and W. Wang (eds.), Blind Source Separation, Signals and Communication Technology, DOI: 10.1007/978-3-642-55016-4_18, © Springer-Verlag Berlin Heidelberg 2014
499
500
A. K. Kattepur and F. Satar
their mixing process. This technique has been used with significant success in various fields such as speech and music processing, sonar, biomedical and financial data [1]. Blind signal processing is statistically based on independent component analysis (ICA) techniques which are based on the assumptions that the original signals are independent and non-Gaussian in nature [2]. The basic instantaneous source separation problem in the time domain can be described as: X = AS + N
(18.1)
Here, X is the observed mixed signal, A is the mixing matrix, S is the source signal, and N is the additive noise. The objective of any blind source separation algorithm is to generate an unmixing matrix W such that the resulting signal Y will be a close estimate of the original source signal S. Y = WX
(18.2)
A number of BSS algorithms widely used include FastICA [3], Infomax [4] and joint approximate diagonalization of eigen-matrices (JADE) [5]. They make use of the second-order or higher order statistics to estimate the unmixing matrix
Data Loading...
