Active Deep Decoding of Linear Codes

Understand

High quality data is essential in deep learning to train a robust model.

• While in other fields data is sparse and costly to collect, in error decoding it is free to query and label thus allowing potential data exploitation.
• Utilizing this fact and inspired by active learning, two novel methods are introduced to improve Weighted Belief Propagation (WBP) decoding.
• These methods incorporate machine-learning concepts with error decoding measures.