Trainable Communication Systems Based on the Binary Neural Network

Abstract

End-to-end learning of the communication system regards the transmitter, channel, and receiver as a neural network-based autoencoder. This approach enables joint optimization of both the transmitter and receiver and can learn to communicate more efficiently than model-based ones. Despite the achieved success, high complexity is the major disadvantage that hinders its further development, while low-precision compression such as one-bit quantization is an effective solution. This study proposed an autoencoder communication system composed of binary neural networks (BNNs), which is based on bit operations and has a great potential to be applied to hardware platforms with very limited computing resources such as FPGAs. Several modifications are explored to further improve the performance. Experiments showed that the proposed BNN-based system can achieve a performance similar to that of the existing neural network-based autoencoder systems while largely reducing the storage and computation complexities.