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.