Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation

Understand

We consider the weighted belief-propagation (WBP) decoder recently proposed by Nachmani et al.

• where different weights are introduced for each Tanner graph edge and optimized using machine learning techniques.
• Our focus is on simple-scaling models that use the same weights across certain edges to reduce the storage and computational burden.
• The main contribution is to show that simple scaling with few parameters often achieves the same gain as the full parameterization.