AEVB-Comm: An Intelligent CommunicationSystem based on AEVBs
This is an incremental improvement for communication systems using deep learning, offering better error rates in noisy conditions.
The paper tackled improving communication system performance by proposing a CNN-based Variational Autoencoder (VAE) with an adjustable beta hyperparameter and higher-dimensional latent space, resulting in reduced Block Error Rate (BLER) compared to AE and traditional methods under AWGN and Rayleigh fading channels.
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 CNN based VAE architecture performs the encoding and modulation at the transmitter, whereas decoding and demodulation at the receiver. Finally, to prove that a continuous latent space-based system designated VAE performs better than the other, various simulation results supporting the same has been conferred under normal and noisy conditions.