.png){kind=icon}
.png){kind=icon}
Return
Fully distributed multi-agent processing strategy applied to vehicular networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.vehcom.2024.100806.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This work explores distributed processing techniques, together with recent advances in multi-agent reinforcement learning (MARL) to implement a fully decentralized reward and decision -making scheme to efficiently allocate resources (spectrum and power). The method targets processes with strong dynamics and stringent requirements such as cellular vehicle-to-everything networks (C-V2X). In our approach, the C-V2X is seen as a strongly connected network of intelligent agents which adopt a distributed reward scheme in a cooperative and decentralized manner, taking into consideration their channel conditions and selected actions in order to achieve their goals cooperatively. The simulation results demonstrate the effectiveness of the developed algorithm, named Distributed Multi -Agent Reinforcement Learning (DMARL), achieving performances very close to that of a centralized reward design, with the advantage of not having the limitations and vulnerabilities inherent to a fully or partially centralized solution.
Keywords:
Distributed processing
Vehicular networks
5G V2X
Spectrum and power allocation
Deep reinforcement learning
Journal
IF:
6.5
Papers:
796
Citations:
3.2K