Performance Analysis of Autoencoders in Wireless Communication Systems with Deep Learning Techniques

Abstract

Wireless experts worldwide have become interested in using Autoencoders (AEs) for modelling communication systems as an end-to-end reconstruction task. This approach optimizes both the transmitter and receiver components simultaneously, offering flexibility and convenience for representing complex channel models. Traditional communication systems rely on conventional models and assumptions that limit their utilization of limited frequency resources and hinder their ability to adapt to new wireless applications. However, with the rise of Artificial Intelligence, new wireless systems are capable of learning from wireless spectrum data and optimizing their performance. In this paper, the use of deep learning with autoencoders is explored to create an end-to-end communication system that replaces traditional transmitter and receiver activities. The autoencoder architecture effectively addresses channel impairments and enhances overall performance. Simulation results indicate that autoencoders surpass conventional communication systems in terms of Block Error Rate performance, even when facing impairments in the autoencoder's channel layer and using different neural network optimization algorithms.