Name
Abstract | ||
|---|---|---|
We extend Gaussian approximation density evolu- tion (DE) techniques from the soft iterative decoding of turbo and low density parity check (LDPC) codes to the performance and convergence analysis of belief propagation (BP) and expectation propagation (EP) in randomly connected very large sparse homogeneous factor graphs. A strict form of the Gaussian approximation allows the use of extrinsic information transfer (EXIT) charts to study the performance and convergence of the algorithms. The result is a graphical tool that design engineers can use to quickly predict the performance and convergence speed of BP or EP applied to these inference problems. We demonstrate the utility of the new tool, and a motivation for the generalization of the results, by showing how it may surprisingly be applied to determine the performance of a scheme for distributed data fusion in a sensor network. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ISIT.2007.4557111 | ISIT |
Keywords | Field | DocType |
Gaussian processes,approximation theory,charts,distributed sensors,graph theory,iterative decoding,parity check codes,sensor fusion,turbo codes,Gaussian approximation density evolution technique,belief propagation,distributed data fusion,extrinsic information transfer chart,homogeneous expectation propagation,low density parity check codes,sensor network,soft iterative decoding,sparse homogeneous factor graphs,turbo codes | Factor graph,Discrete mathematics,Approximation algorithm,Combinatorics,EXIT chart,Low-density parity-check code,Computer science,Turbo code,Gaussian process,Expectation propagation,Belief propagation | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John MacLaren Walsh | 1 | 107 | 17.90 |