异构网络
计算机科学
计算机网络
智能交通系统
可靠性(半导体)
钥匙(锁)
分布式计算
无线
无线网络
电信
计算机安全
工程类
运输工程
量子力学
物理
功率(物理)
作者
Baofeng Ji,Zhenzhen Chen,Shahid Mumtaz,Congzheng Han,Chunguo Li,Hong Wen,Dan Wang
出处
期刊:IEEE Network
[Institute of Electrical and Electronics Engineers]
日期:2022-03-01
卷期号:36 (2): 153-161
被引量:11
标识
DOI:10.1109/mnet.012.2000527
摘要
With increasing traffic congestion and accidents, the Internet of Vehicles (IoV) has become a focus of fifth generation (5G) heterogeneous networks (HetNets) and intelligent transportation systems (ITS). IoV has been studied extensively to improve vehicles, road safety, and efficiency. However, the limited spectrum efficiency and interference caused in the 5G HetNets may require robust technology and architecture. The enabling requirements of IoV in 5G HetNets are elaborated and conceptualized in the article. The deep learning algorithms proposed and used in IoV in 5G HetNets can be achieved to reduce delay and improve transmission reliability. The research progress, advantages, and challenges of deep learning in 5G HetNets IoV are illustrated and explored in detail. Moreover, the overall framework of IoV in 5G HetNets is outlined and compared with the related research, which can become the Foundation for future development of intelligent IoV.
科研通智能强力驱动
Strongly Powered by AbleSci AI