Performance Evaluation of Turbo Autoencoder with Different Interleavers

This paper evaluates the performance of Turbo Autoencoder (TurboAE), an end-to-end jointly trained neural channel encoder and decoder, for different well-known inter-leavers over additive white Gaussian noise (AWGN) channel. TurboAE and Turbo codes share structural similarities. Therefore, in search of suitable interleavers for TurboAE, it is important to examine the interleavers designed for Turbo codes. In this paper, different interleavers are compared for TurboAE, and it is shown that using interleavers such as Quadratic Permutation Polynomial (QPP) and S-random interleavers provides considerable performance gain. The interleaver gain is achieved for both TurboAE made of convolutional neural network (CNN) layers and TurboAE made of recurrent neural networks (RNN) layers. Certain techniques are also proposed to enhance the performance of TurboAE RNN.