Qingmiao Zhang
Orcid: 0000-0002-6435-767X
According to our database1,
Qingmiao Zhang
authored at least 18 papers
between 2019 and 2024.
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Bibliography
2024
IoV-Oriented Integrated Sensing, Computation, and Communication: System Design and Resource Allocation.
IEEE Trans. Veh. Technol., November, 2024
Extended Multi-Component Gated Recurrent Graph Convolutional Network for Traffic Flow Prediction.
IEEE Trans. Intell. Transp. Syst., May, 2024
Boosting vehicular connectivity through resource allocation algorithm based on Heterogeneous Agent Proximal Policy Optimization.
Veh. Commun., 2024
Artificial intelligence in rail transit wireless communication systems: Status, challenges and solutions.
Phys. Commun., 2024
Optimization of resource allocation strategy for high-speed railway based on deep reinforcement learning.
Phys. Commun., 2024
IEEE Internet Things J., 2024
Mobile association scheme based on auction algorithm in heterogeneous wireless networks.
Ad Hoc Networks, 2024
2023
IEEE Internet Things J., October, 2023
IEEE Syst. J., September, 2023
Constrained DRL for Energy Efficiency Optimization in RSMA-Based Integrated Satellite Terrestrial Network.
Sensors, September, 2023
2022
RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm.
Phys. Commun., 2022
Intelligent train control for cooperative train formation: A deep reinforcement learning approach.
J. Syst. Control. Eng., 2022
A Deep Learning Approach for Downlink Sum Rate Maximization in Satellite-Terrestrial Integrated Network.
Proceedings of the International Symposium on Networks, Computers and Communications, 2022
2021
Cluster-based energy-efficient joint user association and resource allocation for B5G ultra-dense network.
Phys. Commun., 2021
2020
Joint 3D Trajectory and Power Optimization for Multiple Antenna Aided NOMA in UAV Networks.
Proceedings of the 2020 International Conference on Wireless Communications and Signal Processing (WCSP), 2020
2019
A Novel QoS-Awared Grid Routing Protocol in the Sensing Layer of Internet of Vehicles Based on Reinforcement Learning.
IEEE Access, 2019