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Handoff Control and Resource Allocation for RAN Slicing in IoT Based on DTN: An Improved Algorithm Based on Actor-Critic Framework
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AI
DOI：10.1109/JIOT.2023.3262953.png)
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Abstract
En 中文
As a three-layer association of Internet of Things Equipment (IoTE)-network slicing (NS)-base station (BS) in radio access network (RAN) slicing, handoff control, and resource allocation has become an important but complicated issue. In addition, the centralized controller has a difficult grasping the network situation in real time. In view of this, the problem of handoff control in the RAN slicing is investigated in the digital twin network (DTN), with the goal of maximizing the long-term utility about user satisfaction and handoff cost. Then, an improved algorithm based on the actor-critic framework is suggested, which is called HCRA. Specifically, the actor component contains neural networks for handoff control and an optimizer for resource allocation, and then the critic component evaluates the handoff and resource allocation actions of the actor component to guide the optimization of actions in the actor component. The simulation results show that HCRA can obtain better performance than benchmark algorithms.
Keywords:
Deep reinforcement learning (DRL)
digital twin network (DTN)
handoff
network slicing (NS)
resource allocation
Journal
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8.9
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