Performance Evaluation of Deep Learning Autoencoder in Single and Multi-Carrier Systems

Abstract

An entire system of Single Carrier Communication and Orthogonal Frequency Division Multiplexing is modelled using an autoencoder. The model employs Deep Neural Networks (DNNs) as the transmitter and receiver, responsible for tasks such as encoding, modulation, demodulation, and decoding. The effectiveness of this approach is demonstrated by its ability to outperform traditional communication systems in real-world scenarios that involve channel and interference effects, as measured by the Block Error Rate. AI-enabled wireless systems can overcome limitations of traditional communication systems by learning from wireless spectrum data and optimizing performance for new wireless applications. The aim of this paper is to examine how autoencoder-based deep learning can enhance the performance of a communication system that employs Single Carrier and OFDM. The architecture effectively addresses channel impairments and improves overall performance. The simulation results suggest that even when the autoencoder's channel layer is affected by impairments, autoencoders still outperform traditional communication systems in terms of BLER performance.