A blind multichannel identification algorithm robust to order overestimation

• Published in: IEEE transactions on signal processing Vol. 50; no. 6; pp. 1449 - 1458
• Main Authors: Gazzah, H., Regalia, P.A., Delmas, J.-P., Abed-Meraim, K.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Bandwidth; Blind equalizers; Blinds; Channels; Communication channels; Computer science; control theory; systems; Control theory. Systems; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Higher order statistics; Image restoration; Impulse response; Information, signal and communications theory; Linear prediction; Mathematical analysis; Modelling and identification; Noise; Noise robustness; Prediction algorithms; Signal and communications theory; Signal processing algorithms; Signal restoration; Signal, noise; Smoothing methods; Statistics; Studies; Telecommunications and information theory; Performance evaluation; Statistical method; Second order; Simulation; Transmission channel; Robustness; Blind identification; System identification; Algorithm; Blind equalization
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2002.1003068

Active research in blind single input multiple output (SIMO) channel identification has led to a variety of second-order statistics-based algorithms, particularly the subspace (SS) and the linear prediction (LP) approaches. The SS algorithm shows good performance when the channel output is corrupted by noise and available for a finite time duration. However, its performance is subject to exact knowledge of the channel order, which is not guaranteed by current order detection techniques. On the other hand, the linear prediction algorithm is sensitive to observation noise, whereas its robustness to channel order overestimation is not always verified when the channel statistics are estimated. We propose a new second-order statistics-based blind channel identification algorithm that is truly robust to channel order overestimation, i.e., it is able to accurately estimate the channel impulse response from a finite number of noisy channel measurements when the assumed order is arbitrarily greater than the exact channel order. Another interesting feature is that the identification performance can be enhanced by increasing a certain smoothing factor. Moreover, the proposed algorithm proves to clearly outperform the LP algorithm. These facts are justified theoretically and verified through simulations.