Generative AI-Enhanced Robust Semantic Communication Architecture for UAV Image Transmission
Date
Authors
Liu, Canpu
Zhou, Li
Deng, Xinfeng
Zhang, Yichi
Li, Nan
Xiong, Jun
Seet, Boon-Chong
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Unmanned aerial vehicle (UAV) wireless image transmission has gained widespread application across various fields due to its flexibility, yet it faces critical challenges such as resource constraints and degradation of reconstruction quality caused by harsh channel conditions. To address these issues, we designed a lightweight semantic communication backbone network that substantially reduces the computational and storage overhead of UAVs through codebook assistance and efficient encoder-decoder design. On this basis, to tackle severe image degradation under adverse channel conditions, we introduced a generative artificial intelligence-based (GAI) enhancement module. Specifically, we developed a semantic refinement network (SRN) that employs an innovative signal-to-noise ratio (SNR) adaptive feature-wise linear modulation (FiLM) layer to dynamically adjust its refinement strategy based on real-time channel quality, fundamentally transforming the image reconstruction paradigm from traditional signal recovery to conditional content generation. Extensive experimental results demonstrate that our proposed framework significantly outperforms the current state-of-the-art method under extreme channel conditions, highlighting its great potential for achieving robust UAV image transmission in challenging operational environments.Description
Keywords
4006 Communications engineering, Semantic communication, Generative AI, UAV image transmission, lightweight model
Source
IEEE Open Journal of the Communications Society, ISSN: 2644-125X (Print); 2644-125X (Online), IEEE. doi: 10.1109/OJCOMS.2026.3660029
Publisher's version
Rights statement
Open Access. Under a Creative Commons License https://creativecommons.org/licenses/by/4.0/