Data-Rate Driven Transmission Strategy for Deep Learning Based Communication Systems

TL;DR

This paper introduces adaptive transmission and generalized data representation schemes to significantly increase data rates in deep learning-based communication systems, demonstrating improved performance and robustness over traditional methods.

Contribution

The paper proposes two novel schemes—adaptive transmission and GDR—that enhance data rate and robustness in DL-based communication autoencoders, with combined benefits.

Findings

Adaptive scheme reduces BLER by 80% compared to conventional methods.

GDR doubles data rate with comparable BLER performance.

Training with varied SNR data improves robustness across channel conditions.

Abstract

Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. One fundamental problem of such systems is how to increase the transmission rate. Two new schemes are proposed to address the limited data rate issue: adaptive transmission scheme and generalized data representation (GDR) scheme. In the first scheme, an adaptive transmission is designed to select the transmission vectors for maximizing the data rate under different channel conditions. The block error rate (BLER) of the first scheme is 80% lower than that of the conventional one-hot vector scheme. This implies that higher data rate can be achieved by the adaptive transmission scheme. In the second scheme, the GDR replaces the conventional one-hot representation. The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance. For example, when the vector size is eight, the proposed GDR scheme can double the date rate of the one-hot vector scheme. Besides, the joint scheme of the two proposed schemes can create further benefits. The effect of signal-to-noise ratio (SNR) is analyzed for these DL-based communication systems. Numerical results show that training the autoencoder using data set with various SNR values can attain robust BLER performance under different channel conditions.