Innovative Variational AutoEncoder for an End-to-End Communication System

Abstract

Powered by deep learning (DL), autoencoders (AE) end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in traditional communication systems and have achieved great success. In this paper, a new probabilistic model, based on the variational autoencoders (VAE), is proposed for short-packet wireless communication systems. Using this new approach, the information messages are represented by the so-called packet hot vectors (PHV), which are inferred by the VAE latent random variables (LRVs). Then only LRVs’ parameters can be transmitted through the physical wireless channel. This results in a significant improvement in spectral efficiency when compared with the pure AE approach, where longer hot vectors are to be transmitted. Specific VAE models have been developed for both binary (BPSK) as well as Quadrature phase shift keying (QPSK) systems. Simulation and numerical results are given to demonstrate the performance of the proposed method in different real scenarios, including Rayleigh and Rician fading channels with Shadowing and Doppler effects. Our simulation and numerical results show that the new proposed VAE with a DL classifier can provide an improved symbol error rate (SER) performance than both the baseline AE and the classical Hamming code with hard decision decoding. Furthermore, as far as the spectral efficiency of the proposed method is concerned, we show that using two channels in the proposed VAE performance exceeds the 7 channels’ baseline AE.

Keywords

• Variational autoencoder
• machine learning
• auto-encoder
• Hamming code
• latent random variable
• probabilistic models