Improving convergence of Belief Propagation decoding
| dc.creator | Stepanov, M. G. | |
| dc.creator | Chertkov, M. | |
| dc.date | 2006-07-25 | |
| dc.date.accessioned | 2026-07-07T08:16:40Z | |
| dc.date.available | 2026-07-07T08:16:40Z | |
| dc.description | The decoding of Low-Density Parity-Check codes by the Belief Propagation (BP) algorithm is revisited. We check the iterative algorithm for its convergence to a codeword (termination), we run Monte Carlo simulations to find the probability distribution function of the termination time, n_it. Tested on an example [155, 64, 20] code, this termination curve shows a maximum and an extended algebraic tail at the highest values of n_it. Aiming to reduce the tail of the termination curve we consider a family of iterative algorithms modifying the standard BP by means of a simple relaxation. The relaxation parameter controls the convergence of the modified BP algorithm to a minimum of the Bethe free energy. The improvement is experimentally demonstrated for Additive-White-Gaussian-Noise channel in some range of the signal-to-noise ratios. We also discuss the trade-off between the relaxation parameter of the improved iterative scheme and the number of iterations. | |
| dc.identifier | https://arxiv.org/abs/cs/0607112 | |
| dc.identifier | http://arxiv.org/abs/cs/0607112 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/133859 | |
| dc.subject | Information Theory | |
| dc.title | Improving convergence of Belief Propagation decoding | |
| dc.type | text |