Abstract
A novel and efficient neural decoder algorithm
is proposed. The proposed decoder is based on the neural
Belief Propagation algorithm and the Automorphism Group. By
combining neural belief propagation with permutations from
the Automorphism Group we achieve near maximum likelihood
performance for High Density Parity Check codes. Moreover, the
proposed decoder significantly improves the decoding complexity,
compared to our earlier work on the topic. We also investigate the
training process and show how it can be accelerated. Simulations
of the hessian and the condition number show why the learning
process is accelerated. We demonstrate the decoding algorithm
for various linear block codes of length up to 63 bits.
is proposed. The proposed decoder is based on the neural
Belief Propagation algorithm and the Automorphism Group. By
combining neural belief propagation with permutations from
the Automorphism Group we achieve near maximum likelihood
performance for High Density Parity Check codes. Moreover, the
proposed decoder significantly improves the decoding complexity,
compared to our earlier work on the topic. We also investigate the
training process and show how it can be accelerated. Simulations
of the hessian and the condition number show why the learning
process is accelerated. We demonstrate the decoding algorithm
for various linear block codes of length up to 63 bits.
| Original language | American English |
|---|---|
| Title of host publication | International Zurich Seminar on Information and Communication (IZS 2018) |
| Place of Publication | Zurich, Switzerland |
| Pages | 40-44 |
| DOIs | |
| State | Published - 21 Feb 2018 |