Intelligent Resource Allocation for Train-to-Train Communication: A Multi-Agent Deep Reinforcement Learning Approach

Intelligent Resource Allocation for Train-to-Train Communication: A Multi-Agent Deep Reinforcement Learning Approach

The application of train-to-train (T2T) communication in urban rail transit is expected to simplify system structure, reduce maintenance costs, and improve operational efficiency. In particular, train-to-wayside (T2W) communication coexist with T2T communication in the train control system based on...

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Bibliographic Details
Main Authors: Junhui Zhao, Yang Zhang, Yiwen Nie, Jin Liu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Train-to-train (T2T) communication
resource allocation
multi-agent deep reinforcement learning (MADRL)
urban rail transit
Online Access:https://ieeexplore.ieee.org/document/8949541/

Internet

https://ieeexplore.ieee.org/document/8949541/

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