An Adaptive Environment-Aware Transformer Autoencoder for UAV-FSO with Dynamic Complexity Control

TL;DR

This paper introduces AEAT-AE, a Transformer-based autoencoder with adaptive layer activation driven by environmental data, enhancing UAV-FSO communication reliability and efficiency in complex, variable conditions.

Contribution

It presents a novel adaptive autoencoder framework that integrates environmental awareness and dynamic complexity control for UAV-FSO systems.

Findings

AEAT-AE reduces bit error rate compared to traditional autoencoders.

The DQN effectively manages computational resources by selecting relevant Transformer layers.

Simulation shows improved performance and efficiency in variable atmospheric conditions.

Abstract

The rise of sixth-generation (6G) wireless networks sets high demands on UAV-assisted Free Space Optical (FSO) communications, where the channel environment becomes more complex and variable due to both atmospheric turbulence and UAV-induced vibrations. These factors increase the challenge of maintaining reliable communication and require adaptive processing methods. Autoencoders are promising as they learn optimal encodings from channel data. However, existing autoencoder designs are generic and lack the specific adaptability and computational flexibility needed for UAV-FSO scenarios. To address this, we propose AEAT-AE (Adaptive Environment-aware Transformer Autoencoder), a Transformer-based framework that integrates environmental parameters into both encoder and decoder via a cross-attention mechanism. Moreover, AEAT-AE incorporates a Deep Q-Network (DQN) that dynamically selects which layers of the Transformer autoencoder to activate based on real-time environmental inputs, effectively balancing performance and computational cost. Simulation results demonstrate that AEAT-AE outperforms conventional methods in bit error rate while maintaining efficient runtime, representing a novel tailored solution for next-generation UAV-FSO communications.