Title :
Convolutional codes from "optimal" linear block codes
Author_Institution :
Boeing Defense & Space Group, Seattle, WA, USA
Abstract :
Both non-recursive and recursive (i.e. turbo codes) convolutional codes (CCs) require careful choices of generators to obtain codes with large minimum distance and ultimately low bit error rate for a given channel characteristic. We first give a constructive method for building a generator matrix of either type of convolutional code based on certain low rate high distance linear block codes. For non-recursive CCs, these linear block codes have optimal dimension given block length n and minimum distance d if 2d>n. For recursive CCs, the linear block codes are constructed to appear random as determined by certain randomness tests.
Keywords :
block codes; convolutional codes; linear codes; matrix algebra; spectral analysis; block length; channel characteristic; generator matrix; large minimum distance codes; low bit error rate; nonrecursive convolutional codes; optimal linear block codes; random codes; randomness tests; recursive convolutional codes; spectral test; turbo codes; Block codes; Clouds; Convolutional codes; Decoding; Ear; Linear code; Sampling methods; Shape; Turbo codes; Upper bound;
Conference_Titel :
Signals, Systems and Computers, 1996. Conference Record of the Thirtieth Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-8186-7646-9
DOI :
10.1109/ACSSC.1996.599065