Robust Semantic Communication for UAV Control with Integrated Trajectory Prediction

This paper presents a novel semantic communication system for efficient and resilient control signal transmission in unmanned aerial vehicles (UAVs). Traditional bit-level transmission methods face challenges under poor channel conditions and dynamic environments, where packet loss and the high dimensionality of control signals impact operational stability. To address these challenges, we propose a long short-term memory (LSTM) based semantic encoding and decoding framework that compresses essential control information. Additionally, a trajectory prediction model is integrated into the semantic framework to refine control signals, thereby enhancing accuracy. Our design includes an encoder at the ground control station (GCS) and a lightweight decoder onboard the UAV, making it suitable for resource-constrained UAVs without onboard control signal processing. Performance evaluations across varying channel conditions demonstrate that the proposed semantic communication method reduces trajectory mean squared error by over 60% and saves communication costs by at least 37% compared to traditional bit-level communication approaches.