0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessIntelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as two promising technologies to boost the performance of wireless communication networks, by proactively altering the wireless communication channels via smart signal reflection and maneuver control, respectively. However, they face different limitations in practice, which restrain their future applications. In this article, we propose new methods to jointly apply IRS and UAV in integrated air-ground wireless networks by exploiting their complementary advantages. Specifically, terrestrial IRS is used to enhance the UAV-ground communication performance, while UAV-mounted IRS is employed to assist in the terrestrial communication. We present their promising application scenarios, new communication design issues as well as potential solutions. In particular, we show that it is practically beneficial to deploy both the terrestrial and aerial IRSs in future wireless networks to reap the benefits of smart reflections in three-dimensional (3D) space.
Changsheng You, Zhenyu Kang, Yong Zeng, Rui Zhang (2021). Enabling Smart Reflection in Integrated Air-Ground Wireless Network: IRS Meets UAV. arXiv (Cornell University), DOI: 10.48550/arxiv.2103.07151.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Preprint
Year
2021
Authors
4
Datasets
0
Total Files
0
Language
English
Journal
arXiv (Cornell University)
DOI
10.48550/arxiv.2103.07151
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access