# A Learning-Based Approach to Address Complexity-Reliability Tradeoff in OS Decoders

arXiv: https://arxiv.org/abs/2103.03860 · HTML: https://mlsanity.com/paper/2103.03860/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning to Decode Linear Codes Using Deep Learning](https://mlsanity.com/paper/1607.04793/) (2016)
- [Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approach](https://mlsanity.com/paper/1802.04741/) (2018)
- [Deep Learning for Frame Error Probability Prediction in BICM-OFDM Systems](https://mlsanity.com/paper/1710.11270/) (2017)
- [Low-Latency Communication with Computational Complexity Constraints](https://mlsanity.com/paper/1909.02740/) (2019)

## Abstract (arXiv)

In this paper, we study the tradeoffs between complexity and reliability for decoding large linear block codes. We show that using artificial neural networks to predict the required order of an ordered statistics based decoder helps in reducing the average complexity and hence the latency of the decoder. We numerically validate the approach through Monte Carlo simulations.