AEVB-Comm: An Intelligent CommunicationSystem based on AEVBs
Raghu Vamshi Hemadri, Akshay Rayaluru, and Rahul Jashvantbhai Pandya

TL;DR
This paper introduces a CNN-based Variational Autoencoder communication system that leverages a continuous latent space with adjustable hyperparameters, achieving improved performance over traditional methods under various noise conditions.
Contribution
It presents a novel CNN-based VAE system with a beta hyperparameter for disentangled latent space and higher-dimensional representation, enhancing communication performance.
Findings
Reduced Block Error Rate (BLER) in simulations
Effective operation under AWGN and Rayleigh fading channels
Superior performance compared to traditional autoencoder systems
Abstract
In recent years, applying Deep Learning (DL) techniques emerged as a common practice in the communication system, demonstrating promising results. The present paper proposes a new Convolutional Neural Network (CNN) based Variational Autoencoder (VAE) communication system. The VAE (continuous latent space) based communication systems confer unprecedented improvement in the system performance compared to AE (distributed latent space) and other traditional methods. We have introduced an adjustable hyperparameter beta in the proposed VAE, which is also known as beta-VAE, resulting in extremely disentangled latent space representation. Furthermore, a higher-dimensional representation of latent space is employed, such as 4n dimension instead of 2n, reducing the Block Error Rate (BLER). The proposed system can operate under Additive Wide Gaussian Noise (AWGN) and Rayleigh fading channels. The…
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Taxonomy
TopicsWireless Signal Modulation Classification · Network Security and Intrusion Detection · Radar Systems and Signal Processing
MethodsAutoencoders · Solana Customer Service Number +1-833-534-1729 · USD Coin Customer Service Number +1-833-534-1729
