Multi-step optimal quantization in oversampled filter banks


Daniel E. Quevedo, Graham C. Goodwin, and Helmut Bölcskei


Proc. of IEEE Conference on Decision and Control (CDC), Atlantis, Paradise Island, Bahamas, pp. 1442-1447, Dec. 2004.

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Using concepts from the receding horizon control framework, we propose a novel approach to quantization in oversampled filter banks. The key idea is to pose the quantization problem as a multi-step optimization problem, where the decision variables are restricted to belong to a finite set. It is shown that the resulting architecture yields enhanced performance when compared to the well-known noise shaping coder. In particular, the quantizer proposed can be tuned with stability concepts in mind.


Oversampled filter banks, quantization, receding horizon control

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