# Achieving Low Complexity Neural Decoders via Iterative Pruning

arXiv: https://arxiv.org/abs/2112.06044 · HTML: https://mlsanity.com/paper/2112.06044/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 1 of 1)

- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)

## Built on by (most cited first, 2 of 2)

- [On the Design and Performance of Machine Learning Based Error Correcting Decoders](https://mlsanity.com/paper/2410.15899/) (2024)
- [On the Optimality of Single-label and Multi-label Neural Network Decoders](https://mlsanity.com/paper/2503.18758/) (2025)

## Abstract (arXiv)

The advancement of deep learning has led to the development of neural decoders for low latency communications. However, neural decoders can be very complex which can lead to increased computation and latency. We consider iterative pruning approaches (such as the lottery ticket hypothesis algorithm) to prune weights in neural decoders. Decoders with fewer number of weights can have lower latency and lower complexity while retaining the accuracy of the original model. This will make neural decoders more suitable for mobile and other edge devices with limited computational power. We also propose semi-soft decision decoding for neural decoders which can be used to improve the bit error rate performance of the pruned network.