Tracking Forecast Memories in stochastic decoders

Authors:
Saeed Sharifi Tehrani;Ali Naderi;Guy-Armand Kamendje;Shie Mannor;Warren J. Gross
Affiliations:
Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada;Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada;Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada;Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada;Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

This paper proposes Tracking Forecast Memories (TFMs) as a novel method for implementing re-randomization and decorrelation of stochastic bit streams in stochastic channel decoders. We show that TFMs are able to achieve decoding performance similar to that of the previous methods in the literature (i.e., edge memories or EMs), but they exhibit much lower hardware complexity. TFMs significantly reduce the area requirements of ASIC implementations of stochastic decoders.