Name
Abstract | ||
|---|---|---|
We propose a recursive algorithm to compute the joint maximum a-posteriori (MAP) probability of a subblock of N consecutive symbols (i.e., a sliding window of length N) of a finite-state discrete-time Markov process of length K/spl ges/N observed in white noise given the whole block is received. This "optimal subblock-by-subblock detector" (OBBD, "vector MAP") is a generalization of the "optimal symbol-by-symbol detector" (OSSD, "symbol-by-symbol MAP"), which is obtained for N=1. The new algorithm improves applications with outer stage processing. This is indicated by investigating the average mutual information of a convolutional coding system. An example shows that the gain (in terms of average mutual information) by using joint probabilities could even exceed the gain by delivering soft OSSD outputs instead of hard outputs.<> |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/26.380096 | IEEE Transactions on Communications |
Keywords | Field | DocType |
Markov processes,convolutional codes,maximum likelihood estimation,probability,signal detection,average mutual information,convolutional coding system,finite-state discrete-time Markov process,gain,joint probabilities,maximum a-posteriori probability,optimal subblock-by-subblock detector,optimal symbol-by-symbol detector,recursive algorithm,sliding window,soft OSSD outputs,subblock,symbol-by-symbol MAP,vector MAP,white noise | Convolutional code,Markov process,Joint probability distribution,Sliding window protocol,Control theory,Algorithm,White noise,Speech recognition,Mutual information,Channel capacity,Mathematics,Vector map | Journal |
Volume | Issue | ISSN |
43 | 2 | 0090-6778 |
Citations | PageRank | References |
6 | 5.61 | 1 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hoeher, P. | 1 | 6 | 5.61 |