| Message from the EiC |
Welcome to the February 2022 issue of VTS Mobile World – your IEEE Vehicular Technology Society newsletter.
This issue features timely articles from the experts—taken from the Connected and Automated Vehicles, Mobile Radio, and Transportation Systems columns in the IEEE Vehicular Technology Magazine December 2021 issue—and also two Early Access papers from the IEEE Transactions on Vehicular Technology and IEEE Open Journal of Vehicular Technology.
As always, I am always available to hear your feedback; just send me a note at Richard.Yu@ieee.org. Read More... |
 |
|
| Connected and Automated Vehicles |
| Tesla Abandons Radar |
| Katrin Sjoberg |
Tesla has once again surprised the industry and the general public, this time by abandoning radar and only relying upon eight camera sensors mounted on the vehicle for its autonomous functionality. Read More... |
|
|
| Mobile Radio |
| Virtualization Achieved for 5G |
| Claudio Casetti |
Verizon and Samsung Electronics completed an end-to-end, fully virtualized 5G data session using C-band spectrum on a live network. This trial used Samsung C-band 64T64R massive MIMO radios that support digital/dynamic beamforming, single-user MIMO, multiple-user MIMO, and dual connectivity and carrier aggregation. Read More... |
|
|
| Transportation Systems |
| The First Vectron Locomotives in Eastern Europe |
| Bih-Yuan Ku |
Railpool, one of Europe’s leading rail vehicle rental companies, has ordered 20 Vectron multisystem (MS) locomotives from Siemens Mobility. These locomotives are intended for services in 11 countries along Europe’s Eastern Corridor. Read More... |
|
|
| From the IEEE Open Journal of Vehicular Technology |
| Satellite- and Cache-Assisted UAV |
| Dinh-Hieu Tran, Symeon Chatzinotas, and Björn Ottersten |
This paper considers Low Earth Orbit (LEO) satellite- and cache-assisted unmanned aerial vehicle (UAV) communications for content delivery in terrestrial networks, which shows great potential for next-generation systems to provide ubiquitous connectivity and high capacity. Read More... |
|
|
| From the IEEE Transactions on Vehicular Technology |
| Resource Allocation for Dynamic Vehicular Networks |
| Ying He, Yuhang Wang, Qiuzhen Lin, and Jianqiang Li |
In this paper, the authors propose a general framework that can enable fast-adaptive resource allocation for dynamic vehicular environments. They combine hierarchical reinforcement learning with meta learning, which makes our proposed framework quickly adapt to a new environment by only fine-tuning the top-level master network, and meanwhile the low-level sub-networks can make the right resource allocation policy. Read More... |
|
|
|
|
|
Editor-in-Chief
F. Richard Yu
|
|
|
