Deep reinforcement learning based edge computing network aided resource allocation algorithm for smart grid
Y Chi, Y Zhang, Y Liu, H Zhu, Z Zheng, R Liu… - Ieee …, 2022 - ieeexplore.ieee.org
The dramatic increase in the volume of users and services makes scheduling network
resources for smart grids a key challenge. Network slicing is an important technology to …
resources for smart grids a key challenge. Network slicing is an important technology to …
Edgeslice: Slicing wireless edge computing network with decentralized deep reinforcement learning
5G and edge computing will serve various emerging use cases that have diverse
requirements of multiple resources, eg, radio, transportation, and computing. Network slicing …
requirements of multiple resources, eg, radio, transportation, and computing. Network slicing …
Utility optimization for resource allocation in multi-access edge network slicing: A twin-actor deep deterministic policy gradient approach
To achieve the service-oriented features of the 5G, network slicing aims to create logical
virtual networks where multiple services are provided on a common physical infrastructure …
virtual networks where multiple services are provided on a common physical infrastructure …
Network Resource Allocation Algorithm using Reinforcement Learning Policy-based Network in a Smart Grid Scenario
Z Zheng, Y Han, Y Chi, F Yuan, W Cui, H Zhu, Y Zhang… - Electronics, 2023 - mdpi.com
The exponential growth in user numbers has resulted in an overwhelming surge in data that
the smart grid must process. To tackle this challenge, edge computing emerges as a vital …
the smart grid must process. To tackle this challenge, edge computing emerges as a vital …
Deep reinforcement learning-based service-oriented resource allocation in smart grids
L Xi, Y Wang, Y Wang, Z Wang, X Wang, Y Chen - IEEE Access, 2021 - ieeexplore.ieee.org
Resource allocation has a direct and profound impact on the performance of resource-
limited smart grids with diversified services that need to be timely processed. In this paper …
limited smart grids with diversified services that need to be timely processed. In this paper …
A sub-action aided deep reinforcement learning framework for latency-sensitive network slicing
Network slicing is a core technique of fifth-generation (5G) systems and beyond. To
maximize the number of accepted network slices with limited hardware resources, service …
maximize the number of accepted network slices with limited hardware resources, service …
Real-time resource slicing for 5G RAN via deep reinforcement learning
R Xi, X Chen, Y Chen, Z Li - 2019 IEEE 25th International …, 2019 - ieeexplore.ieee.org
With the rapid growth of Internet of Things (IoT), network slicing is regarded as an important
technology to support the multi-users' needs for 5G mobile network. Network slicing allows …
technology to support the multi-users' needs for 5G mobile network. Network slicing allows …
Reinforcement learning based resource management for network slicing
Network slicing to create multiple virtual networks, called network slice, is a promising
technology to enable networking resource sharing among multiple tenants for the 5th …
technology to enable networking resource sharing among multiple tenants for the 5th …
Data-driven dynamic resource scheduling for network slicing: A deep reinforcement learning approach
Network slicing is designed to support a variety of emerging applications with diverse
performance and flexibility requirements, by dividing the physical network into multiple …
performance and flexibility requirements, by dividing the physical network into multiple …
AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
Network slicing is a key technology for 5G networks, which divides the traditional physical
network into multiple independent logical networks to meet the diverse requirements of end …
network into multiple independent logical networks to meet the diverse requirements of end …
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