Name
Abstract | ||
|---|---|---|
Recursive systematic convolutional encoders have been shown to play a crucial role in the design of turbo codes. We recall some properties of binary convolutional encoders and apply them to a search for good constituent convolutional codes of turbo codes. Tables of the “best” recursive systematic convolutional encoders found are presented for various rates, together with the average bit-error probability performances of some turbo codes using them |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1109/26.718548 | IEEE Transactions on Communications |
Keywords | Field | DocType |
binary sequences,concatenated codes,convolutional codes,error statistics,finite state machines,group theory,search problems,average bit-error probability,best codes,binary convolutional encoders,convolutional codes,finite state machine,good constituent codes,group theory,recursive systematic convolutional encoders,turbo codes construction | Concatenated error correction code,BCJR algorithm,Convolutional code,Computer science,Block code,Turbo code,Serial concatenated convolutional codes,Algorithm,Theoretical computer science,Electronic engineering,Linear code,Turbo equalizer | Journal |
Volume | Issue | ISSN |
46 | 9 | 0090-6778 |
Citations | PageRank | References |
64 | 16.05 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Benedetto | 1 | 883 | 167.24 |
| Roberto Garello | 2 | 208 | 31.47 |
| Guido Montorsi | 3 | 1032 | 179.05 |