Channel-Encoding-using-Neural-Network

End-to-end deep learning for communication systems, i.e., the complete process of training the embeddings for the messages, dense NN to convert the embeddings into signal constellation and training the decoder to decode the received corrupted signal is carried out at one go. These encoder-decoder systems can achieve bit error rates which come close to practical baseline techniques if they are used for over-the-air transmissions. This is promising since complex encoding and decoding functions can be learned on the fly without extensive communication-theoretic analysis and design, possibly enabling future communication systems to better cope with new and changing channel scenarios.