Deep Learning for Channel Coding via Neural Mutual Information Estimation
This addresses a key limitation for deploying learned communication systems in real-world scenarios where channel models are unavailable, offering a practical solution for researchers and engineers.
The paper tackles the problem of training end-to-end deep learning communication systems without requiring a differentiable channel model, by using a neural mutual information estimator to optimize the encoder based on channel samples, achieving performance comparable to state-of-the-art methods with perfect channel knowledge.
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.