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Blind separation of mutually correlated sources using precoders
Yong Xiang1, Sze Kui Ng, Van Khanh Nguyen
1School of Engineering, Deakin University, Geelong, Vic 3217, Australia. yxiang@deakin.edu.au
IEEE Transactions on Neural Networks
|November 26, 2009
Summary
This study introduces precoders for blind source separation (BSS) of correlated signals. By zeroing cross-correlations, this method enables effective source separation using unique signal properties.
Area of Science:
- Signal Processing
- Information Theory
- Communications Engineering
Background:
- Blind Source Separation (BSS) is challenging with correlated sources.
- Existing BSS methods often assume source independence.
Purpose of the Study:
- To develop a novel BSS approach for mutually correlated signals.
- To leverage precoding techniques for enhanced source separation.
Main Methods:
- Utilizing precoders at transmitters to manipulate signal correlations.
- Designing precoders to force zero cross-correlation coefficients at specific time lags.
- Developing a subspace-based algorithm for separation based on modified correlations.
Main Results:
- Demonstrated that properly designed precoders can induce desired cross-correlation properties.
- Successfully separated mutually correlated sources using the proposed subspace algorithm.
- Simulation examples validated the effectiveness of the developed BSS method.
Conclusions:
- Precoder-based manipulation of signal correlations offers a viable path for BSS of correlated sources.
- The proposed subspace algorithm provides an effective solution for this challenging BSS problem.
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