MacWilliams identities for terminated convolutional codes

Shearer and McEliece showed that there is no MacWilliams identity for the free distance spectra of orthogonal linear convolutional codes. We show that on the other hand there does exist a MacWilliams identity between the generating functions of the weight distributions per unit time of a linear conv...

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Bibliographic Details
Main Author: Forney, G. David, Jr (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2012-09-20T17:52:43Z.
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Online Access:Get fulltext
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100 1 0 |a Forney, G. David, Jr.  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. Laboratory for Information and Decision Systems  |e contributor 
100 1 0 |a Forney, G. David, Jr.  |e contributor 
245 0 0 |a MacWilliams identities for terminated convolutional codes 
260 |b Institute of Electrical and Electronics Engineers (IEEE),   |c 2012-09-20T17:52:43Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/73077 
520 |a Shearer and McEliece showed that there is no MacWilliams identity for the free distance spectra of orthogonal linear convolutional codes. We show that on the other hand there does exist a MacWilliams identity between the generating functions of the weight distributions per unit time of a linear convolutional code C and its orthogonal code C[superscript ⊥], and that this distribution is as useful as the free distance spectrum for estimating code performance. These observations are similar to those made recently by Bocharova et al.; however, we focus on terminating by tail-biting rather than by truncation. 
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655 7 |a Article 
773 |t 2010 IEEE International Symposium on Information Theory Proceedings (ISIT)