# Discriminative Mutual Information Estimation for the Design of Channel Capacity Driven Autoencoders

arXiv: https://arxiv.org/abs/2111.07606 · HTML: https://mlsanity.com/paper/2111.07606/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2021.

## Built on by (most cited first, 1 of 1)

- [Code Rate Optimization via Neural Polar Decoders](https://mlsanity.com/paper/2506.15836/) (2025)

## Abstract (arXiv)

The development of optimal and efficient machine learning-based communication systems is likely to be a key enabler of beyond 5G communication technologies. In this direction, physical layer design has been recently reformulated under a deep learning framework where the autoencoder paradigm foresees the full communication system as an end-to-end coding-decoding problem. Given the loss function, the autoencoder jointly learns the coding and decoding optimal blocks under a certain channel model. Because performance in communications typically refers to achievable rates and channel capacity, the mutual information between channel input and output can be included in the end-to-end training process, thus, its estimation becomes essential. In this paper, we present a set of novel discriminative mutual information estimators and we discuss how to exploit them to design capacity-approaching codes and ultimately estimate the channel capacity.