Name
Title | ||
|---|---|---|
Belief propagation with Gaussian approximation for joint channel estimation and decoding |
Abstract | ||
|---|---|---|
In order to increase the performance of joint channel estimation and decoding through belief propagation on factor graphs, we approximate the distribution of channel estimate in the factor graph as a mixture of Gaussian distributions. The result is a continuous downward and upward message propagation in the factor graph instead of discrete probability distributions. Using continuous downward messages, the computation complexity of belief propagation is reduced without performance degradation. With both continuous upward and downward messages, belief propagation almost achieves the same performance as expectation-maximization under good initialization and outperforms it under bad initialization. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/PIMRC.2008.4699839 | PIMRC |
Keywords | Field | DocType |
continuous downward message,belief networks,factor graph,gaussian approximation,belief propagation,approximation theory,channel coding,joint channel estimation,gaussian distribution,computational complexity,computation complexity,discrete probability distribution,decoding,channel estimation,bit error rate,probability distribution,mixture of gaussians,estimation,expectation maximization,quantization | Factor graph,Mathematical optimization,Computer science,Approximation theory,Algorithm,Real-time computing,Probability distribution,Gaussian,Decoding methods,Initialization,Computational complexity theory,Belief propagation | Conference |
ISBN | Citations | PageRank |
978-1-4244-2644-7 | 4 | 0.47 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Liu | 1 | 12 | 2.10 |
| Loïc Brunel | 2 | 147 | 14.09 |
| Joseph Jean Boutros | 3 | 228 | 24.65 |