A CAM-based decoder of convolutionally-encoded data

Author

Wu, Yu-Jhih ; Alston, Michael D. ; Chau, Paul M.

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA

fYear

1991

fDate

11-14 Jun 1991

Firstpage

2955

Abstract

Presented is a new VLSI architecture for decoding convolutionally encoded digital data for short (K⩽=9) as well as long (K>9) constraint-length (K) codes. Shift-invariant relationships between clusters of channel symbols derived from the particular code in use are used to generate error pattern sequence syndromes which address a bank consisting of content addressable memory (CAM) containing channel error scenarios. This approach combined with decision feedback techniques allows the correction of certain classes of channel symbol errors without hypothesizing the state of the encoder

Keywords

VLSI; computer architecture; content-addressable storage; decoding; error correction; special purpose computers; CAM-based decoder; VLSI architecture; channel error scenarios; content addressable memory; decision feedback techniques; decoding convolutionally encoded digital data; error correction; error pattern sequence syndromes; CADCAM; Computer aided manufacturing; Converters; Convolutional codes; Decoding; Equations; Error correction; Shift registers; Very large scale integration; Viterbi algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1991., IEEE International Sympoisum on

Print_ISBN

0-7803-0050-5

Type

conf

DOI

10.1109/ISCAS.1991.176165

Filename

176165