Deep Autoencoder-Based Constellation Design in Multiple Access Channels

TL;DR

This paper introduces a deep autoencoder framework for designing constellations in multiple access channels, improving performance by mitigating interference and adapting to complex multi-user scenarios.

Contribution

It presents a novel deep autoencoder-based method for constellation design in MAC, capable of handling scenarios without analytical solutions and outperforming traditional constellations.

Findings

DAE-designed constellations reduce symbol error rate.

DAE approach enhances sum capacity in MAC.

Method outperforms traditional constellations in various settings.

Abstract

In multiple access channels (MAC), multiple users share a transmission medium to communicate with a common receiver. Traditional constellations like quadrature amplitude modulation are optimized for point-to-point systems and lack mechanisms to mitigate inter-user interference, leading to suboptimal performance in MAC environments. To address this, we propose a novel framework for constellation design in MAC that employs deep autoencoder (DAE)-based communication systems. This approach intelligently creates flexible constellations aware of inter-user interference, reducing symbol error rate and enhancing the constellation-constrained sum capacity of the channel. Comparisons against analytically derived constellations demonstrate that DAE-designed constellations consistently perform best or equal to the best across various system parameters. Furthermore, we apply the DAE to scenarios where no analytical solutions have been developed, such as with more than two users, demonstrating the adaptability of the model.