Name
Title | ||
|---|---|---|
Joint channel estimation and decoding using Gaussian approximation in a factor graph over multipath channel |
Abstract | ||
|---|---|---|
Joint channel estimation and decoding using belief propagation on factor graphs requires the quantization of probability densities since continuous parameters are involved. We propose to replace these densities by standard messages where the channel estimate is accurately modeled as a Gaussian mixture over multipath channel. Upward messages include symbol extrinsic information and downward messages carry mean values and variances for the Gaussian modeled channel estimate. Such unquantized message propagation leads to a complexity reduction and a performance improvement. Over multipath channel, the proposed belief propagation almost achieves the performance of iterative APP equalizer and outperforms MMSE equalizer. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/PIMRC.2009.5449857 | PIMRC |
Keywords | Field | DocType |
multipath channel,upward messages,factor graph,gaussian approximation,belief propagation,joint channel estimation,symbol extrinsic information,unquantized message propagation,multipath channels,least mean squares methods,gaussian processes,downward messages,iterative app equalizer,graph theory,decoding,iterative methods,channel estimation,complexity reduction,probability density,gaussian distribution,quantization | Factor graph,Computer science,Algorithm,Communication channel,Real-time computing,Theoretical computer science,Adaptive equalizer,Gaussian,Gaussian process,Decoding methods,Quantization (signal processing),Belief propagation | Conference |
ISBN | Citations | PageRank |
978-1-4244-5123-4 | 5 | 0.47 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Liu | 1 | 12 | 2.10 |
| Loïc Brunel | 2 | 147 | 14.09 |
| Joseph Jean Boutros | 3 | 228 | 24.65 |