# Deep Learning for Channel Coding via Neural Mutual Information Estimation

## TL;DR

This paper introduces a neural mutual information estimator to optimize communication system encoders using only channel samples, eliminating the need for a known channel model and achieving performance comparable to traditional methods.

## Contribution

It presents a novel mutual information-based training method for end-to-end communication systems that does not require a differentiable channel model.

## Key findings

- Achieves state-of-the-art performance without explicit channel models
- Uses neural mutual information estimation with only channel samples
- Matches performance of traditional end-to-end learning with known channels

## Abstract

End-to-end deep learning for communication systems, i.e., systems whose encoder and decoder are learned, has attracted significant interest recently, due to its performance which comes close to well-developed classical encoder-decoder designs. However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks. In real-world scenarios, such a channel model is hardly available and often the channel density is not even known at all. Some works, therefore, focus on a generative approach, i.e., generating the channel from samples, or rely on reinforcement learning to circumvent this problem. We present a novel approach which utilizes a recently proposed neural estimator of mutual information. We use this estimator to optimize the encoder for a maximized mutual information, only relying on channel samples. Moreover, we show that our approach achieves the same performance as state-of-the-art end-to-end learning with perfect channel model knowledge.