TY - GEN
T1 - Visual Event Detection over AI-Edge LEO Satellites with AoI Awareness
AU - Basnayaka, Chathuranga M. Wijerathna
AU - Lee, Haeyoung
AU - Kourtessis, Pandelis
AU - Senior, John M.
AU - Sooriarachchi, Vishalya P.
AU - Jayakody, Dushantha Nalin K.
AU - Beko, Marko
AU - Shin, Seokjoo
N1 - © 2026 IEEE. This is the accepted manuscript version of a conference proceeding which has been published in final form at https://doi.org/10.1109/ICOIN68469.2026.11480568
PY - 2026/4/28
Y1 - 2026/4/28
N2 - 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 detection performance, this work introduces the age of misclassified information (AoMI) metric and a threshold based AoI analysis that measures the proportion of users meeting application specific timeliness requirements. Simulation results show that the proposed JSCC scheme provides higher inference accuracy, lower average AoMI, and greater threshold compliance than the conventional SSCC baseline, enabling semantic communication in AI native LEO satellite networks for 6G and beyond.
AB - 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 detection performance, this work introduces the age of misclassified information (AoMI) metric and a threshold based AoI analysis that measures the proportion of users meeting application specific timeliness requirements. Simulation results show that the proposed JSCC scheme provides higher inference accuracy, lower average AoMI, and greater threshold compliance than the conventional SSCC baseline, enabling semantic communication in AI native LEO satellite networks for 6G and beyond.
KW - Age of Information
KW - Deep Joint Source and Channel Coding
KW - Semantic Communication
U2 - 10.1109/ICOIN68469.2026.11480568
DO - 10.1109/ICOIN68469.2026.11480568
M3 - Conference contribution
SN - 9798331578978
T3 - International Conference on Information Networking
SP - 571
EP - 576
BT - 2026 40th International Conference on Information Networking (ICOIN)
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 2026 40th International Conference on Information Networking (ICOIN)
Y2 - 14 January 2026 through 16 January 2026
ER -