article · University of Washington Tacoma Digital Commons (University of Washington Tacoma) · 2004
High-Rate Recursive Convolutional Codes for Concatenated Channel Codes
Laddomada, Massimiliano, Daneshgaran, F., Mondin, M.
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Abstract
This letter presents the results of the search for optimum punctured recursive convolutional codes (RCCs) of rate k/k+1, for k=2,...,8, suitable for concatenated channel codes whose constituent encoders are recursive, systematic convolutional codes. The mother codes that are punctured are rate-1/2 RCCs proposed for use in parallel and/or serial concatenation schemes. Extensive tables of systematic and nonsystematic puncturing patterns, optimized relative to various objective functions suitable for concatenated channel codes, are presented for several mother codes.
Cite this paper
Laddomada, M., Daneshgaran, F., & Mondin, M. (2004). High-Rate Recursive Convolutional Codes for Concatenated Channel Codes. University of Washington Tacoma Digital Commons (University of Washington Tacoma).
Laddomada, Massimiliano, et al. “High-Rate Recursive Convolutional Codes for Concatenated Channel Codes.” University of Washington Tacoma Digital Commons (University of Washington Tacoma), 2004.
Laddomada, Massimiliano, F. Daneshgaran, and M. Mondin. 2004. “High-Rate Recursive Convolutional Codes for Concatenated Channel Codes.” University of Washington Tacoma Digital Commons (University of Washington Tacoma).
Laddomada, M., Daneshgaran, F. and Mondin, M. (2004) ‘High-Rate Recursive Convolutional Codes for Concatenated Channel Codes’, University of Washington Tacoma Digital Commons (University of Washington Tacoma).
@article{laddomada2004high,
title = {{High-Rate Recursive Convolutional Codes for Concatenated Channel Codes}},
author = {Laddomada, Massimiliano and Daneshgaran, F. and Mondin, M.},
journal = {University of Washington Tacoma Digital Commons (University of Washington Tacoma)},
year = {2004}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.