Context-Aware Information Transfer via Digital Semantic Communication in UAV-Based Networks
Poorvi Joshi, Mohan Gurusamy (National University of Singapore)

TL;DR
This paper introduces DSC-UAV, a novel semantic communication framework for UAV networks in smart cities, enhancing data relevance and transmission efficiency through context-aware encoding and reinforcement learning-based optimization.
Contribution
It proposes a comprehensive semantic communication model for UAVs that combines prompt-aware encoding, dynamic relaying, and mobility-optimized reinforcement learning, improving transmission quality and efficiency.
Findings
Up to 22% improvement in semantic-structural similarity.
14% reduction in Age of Information (AoI).
Enhanced stability and precision in resource allocation.
Abstract
In smart cities, bandwidth-constrained Unmanned Aerial Vehicles (UAVs) often fail to relay mission-critical data in time, compromising real-time decision-making. This highlights the need for faster and more efficient transmission of only the most relevant information. To address this, we propose DSC-UAV model, leveraging a context-adaptive Digital Semantic Communication (DSC) framework. This model redefines aerial data transmission through three core components: prompt-aware encoding, dynamic UAV-enabled relaying, and user mobility-optimized reinforcement learning. Ground users transmit context-driven visual content. Images are encoded via Vision Transformer combined with a prompt-text encoder to generate semantic features based on the desired context (generic or object-specific). These features are then quantized and transmitted over a UAV network that dynamically relays the data.…
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Taxonomy
TopicsUAV Applications and Optimization · Age of Information Optimization · IoT and Edge/Fog Computing
