# What Can Machine Learning Teach Us about Communications?

arXiv: https://arxiv.org/abs/1901.07592 · HTML: https://mlsanity.com/paper/1901.07592/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Autoregressive Belief Propagation for Decoding Block Codes](https://mlsanity.com/paper/2103.11780/) (2021)
- [Bit Error and Block Error Rate Training for ML-Assisted Communication](https://mlsanity.com/paper/2210.14103/) (2022)
- [DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems](https://mlsanity.com/paper/2212.07816/) (2022)
- [Neural Network Decoders for Permutation Codes Correcting Different Errors](https://mlsanity.com/paper/2206.03315/) (2022)

## Abstract (arXiv)

Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-the-shelf learning packages to design high-performance systems based on simulations. Prior to the current revolution in machine learning, the majority of communication engineers were quite aware that system parameters (such as filter coefficients) could be learned using stochastic gradient descent. It was not at all clear, however, that more complicated parts of the system architecture could be learned as well. In this paper, we discuss the application of machine-learning techniques to two communications problems and focus on what can be learned from the resulting systems. We were pleasantly surprised that the observed gains in one example have a simple explanation that only became clear in hindsight. In essence, deep learning discovered a simple and effective strategy that had not been considered earlier.