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Deep learning (DL)-based autoencoder is a potential architecture to implementend-to-end communication systems. In this letter, we first give a briefintroduction to the autoencoder-represented communication system. Then, wepropose a novel generalized data representation (GDR) aiming to improve thedata rate of DL-based communication systems. Finally, simulation results showthat the proposed GDR scheme has lower training complexity, comparable blockerror rate performance and higher channel capacity than the conventionalone-hot vector scheme. Furthermore, we investigate the effect ofsignal-to-noise ratio (SNR) in DL-based communication systems and prove thattraining at a high SNR could produce a good training performance forautoencoder.

A Generalized Data Representation and Training-Performance Analysis for Deep Learning-Based Communications Systems