Resource Allocation for D2D-Enabled Vehicular Communications

By Le Liang, Geoffrey Y. Li, Wei Xu. IEEE Transactions on Communications, 2017

368 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8d58881c343f5698d56a64b3fb0e7211663b8190

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Geoffrey Y. Li
2. Hao Ye
3. Biing-Hwang Fred Juang
4. Le Liang
5. Rose Qingyang Hu
6. Yi Qian
7. Shengjie Xu
8. Sohan Gyawali

Citations in the corpus

• Challenges and Solutions for Cellular Based V2X Communications (2021, background): “Similar to [38] and [39], the proposed scheme leads to the poor spectrum utilization as a spectrum of one cellular user equipment can be shared with the only single device user equipment.”
• Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning (2019, methodology): “Sum ergodic capacity of V2I links has been maximized with V2V reliability guarantee using large-scale fading channel information in [17] or CSI from periodic feedback in [18].”
• Deep Reinforcement Learning Based Resource Allocation for V2V Communications (2018, methodology): “In [8], a resource allocation scheme has been developed only based on the slowly varying large-scale fading information of the channel and the sum V2I ergodic capacity is optimized with V2V reliability guaranteed.”