| Performance Evaluation and Analysis of Deep Learning Autoencoder-Based Wireless Communication System |
| Paper ID : 1109-ICEEM2023 (R1) |
| Authors |
|
Eman Ismail Abdel Azeem *1, ABDELHAY ALI2, omar A.M. Aly3, Mohammed Abo_zahhad2 1Dept. of Electrical Engineering, Faculty of Engineering, Assuit university, Assuit, Egypt 2Department of Electrical Engineering, Faculty of Engineering, Assuit University, Assuit, Egypt 3Dept. Of Electrical Engineering, Faculty of Engineering, Assuit university, Assuit, Egypt |
| Abstract |
| Recent advancements in deep learning have led to the emergence of autoencoder-based (AE) wireless communication systems, presenting a promising approach to tackle the challenges posed by conventional mathematical models. In this research, a thorough evaluation and analysis of the performance of deep learning AE-based wireless communication systems is conducted. Specifically, our investigation focuses on employing the additive white Gaussian noise (AWGN) channel and explores the impact of varying signal-to-noise ratio (SNR) conditions during the AE training process on system performance. The obtained results show that training the autoencoder under diverse SNR conditions, particularly with an extended number of epochs, surpasses the performance of a fixed trained autoencoder in terms of block error rate (BLER). Additionally, a comparison of BLER between multiple AE models and traditional mathematical representations used in communication systems is conducted. The obtained findings indicate that deep learning AE-based wireless communication systems exhibit promising performance compared to conventional models, underscoring their potential as an effective solution for wireless communication systems. |
| Keywords |
| Autoencoder, Deep learning, Wireless communication systems, Performance evaluation, AWGN channel |
| Status: Accepted (Oral Presentation) |