Deep learning (DL)-based autoencoder is a potential architecture to implement end-to-end communication systems. In this letter, we first give a brief introduction to the autoencoder-represented communication system. Then, we propose a novel generalized data representation (GDR) aiming to improve the data rate of DL-based communication systems. Finally, simulation results show that the proposed GDR scheme has lower training complexity, comparable block error rate performance and higher channel capacity than the conventional one-hot vector scheme. Furthermore, we investigate the effect of signal-to-noise ratio (SNR) in DL-based communication systems and prove that training at a high SNR could produce a good training performance for autoencoder.
@article{arxiv.1806.10333,
title = {A Generalized Data Representation and Training-Performance Analysis for Deep Learning-Based Communications Systems},
author = {Xiao Chen and Liang Wu and Zaichen Zhang},
journal= {arXiv preprint arXiv:1806.10333},
year = {2018}
}