# Active Deep Decoding of Linear Codes

## TL;DR

This paper introduces active learning-inspired methods to enhance Weighted Belief Propagation decoding of linear codes, achieving significant FER improvements without increasing decoding complexity by smart data sampling.

## Contribution

It presents novel active deep decoding techniques that incorporate error decoding measures, improving performance of WBP for BCH codes without added complexity.

## Key findings

- Up to 0.4dB improvement in the waterfall region.
- Up to 1.5dB improvement in the error floor region.
- Effective data sampling enhances decoding performance.

## Abstract

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. For BCH(63,36), (63,45) and (127,64) codes, with cycle-reduced parity-check matrices, improvement of up to 0.4dB at the waterfall region, and of up to 1.5dB at the errorfloor region in FER, over the original WBP, is demonstrated by smartly sampling the data, without increasing inference (decoding) complexity. The proposed methods constitutes an example guidelines for model enhancement by incorporation of domain knowledge from error-correcting field into a deep learning model. These guidelines can be adapted to any other deep learning based communication block.