Visual Event Detection over AI-Edge LEO Satellites with AoI Awareness
Chathuranga M. Wijerathna Basnayaka, Haeyoung Lee, Pandelis Kourtessis, John M. Senior, Vishalya P. Sooriarachchi, Dushantha Nalin K. Jayakody, Marko Beko, Seokjoo Shin

TL;DR
This paper introduces a deep joint source-channel coding scheme for AI-enabled LEO satellite networks, enhancing visual event detection by transmitting only meaningful features, which improves accuracy and timeliness over traditional methods.
Contribution
It proposes a novel DJSCC-based downlink scheme optimized for semantic visual inference in AI-native LEO satellite networks, addressing link constraints and improving performance.
Findings
DJSCC achieves higher inference accuracy than SSCC.
DJSCC results in lower average AoMI, indicating fresher information.
The scheme ensures greater threshold compliance for timeliness.
Abstract
Non terrestrial networks (NTNs), particularly low Earth orbit (LEO) satellite systems, play a vital role in supporting future mission critical applications such as disaster relief. Recent advances in artificial intelligence (AI)-native communications enable LEO satellites to act as intelligent edge nodes capable of on board learning and task oriented inference. However, the limited link budget, coupled with severe path loss and fading, significantly constrains reliable downlink transmission. This paper proposes a deep joint source-channel coding (DJSCC)-based downlink scheme for AI-native LEO networks, optimized for goal-oriented visual inference. In the DJSCC approach, only semantically meaningful features are extracted and transmitted, whereas conventional separate source-channel coding (SSCC) transmits the original image data. To evaluate information freshness and visual event…
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Taxonomy
TopicsAge of Information Optimization · Satellite Communication Systems · IoT Networks and Protocols
