Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Network
Min Hao, Yanbing Xu, Maoqiang Wu, Jinglin Huang, Chen Shang, Jiacheng Wang, Ruichen Zhang, Jiawen Kang, Dusit Niyato, Zhu Han, Wei Ni

TL;DR
This paper explores integrating cross-view localization with 6G space-air-ground networks, proposing a split-inference framework that improves localization accuracy, speed, and privacy by leveraging SAGIN's distributed resources.
Contribution
It introduces a novel split-inference framework for CVL in 6G SAGIN, optimizing communication, computation, and privacy for enhanced performance.
Findings
Higher localization accuracy achieved
Faster processing speed demonstrated
Effective joint optimization of resources and privacy
Abstract
Recently, visual localization has become an important supplement to improve localization reliability, and cross-view approaches can greatly enhance coverage and adaptability. Meanwhile, future 6G will enable a globally covered mobile communication system, with a space-air-ground integrated network (SAGIN) serving as key supporting architecture. Inspired by this, we explore an integration of cross-view localization (CVL) with 6G SAGIN, thereby enhancing its performance in latency, energy consumption, and privacy protection. First, we provide a comprehensive review of CVL and SAGIN, highlighting their capabilities, integration opportunities, and potential applications. Benefiting from the fast and extensive image collection and transmission capabilities of the 6G SAGIN architecture, CVL achieves higher localization accuracy and faster processing speed. Then, we propose a split-inference…
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Taxonomy
TopicsAdvanced Wireless Communication Technologies · UAV Applications and Optimization · Indoor and Outdoor Localization Technologies
