Abstract
In the fifth-generation of mobile communications, network slicing is used to provide an optimal network for various services as a slice. In this paper, we propose a radio access network (RAN) slicing method that flexibly allocates RAN resources using deep reinforcement learning (DRL). In RANs, the number of slices controlled by a base station fluctuates in terms of user ingress and egress from the base station coverage area and service switching on the respective sets of user equipment. Therefore, when resource allocation depends on the number of slices, resources cannot be allocated when the number of slices changes. We consider a method that makes optimal-resource allocation independent of the number of slices. Resource allocation is optimized using DRL, which learns the best action for a state through trial and error. To achieve independence from the number of slices, we show a design for a model that manages resources on a one-slice-by-one-agent basis using Ape-X, which is a DRL method. In Ape-X, because agents can be employed in parallel, models that learn various environments can be generated through trial and error of multiple environments. In addition, we design a model that satisfies the slicing requirements without over-allocating resources. Based on this design, it is possible to optimally allocate resources independently of the number of slices by changing the number of agents. In the evaluation, we test multiple scenarios and show that the mean satisfaction of the slice requirements is approximately 97%.
Highlights
The fifth-generation mobile communications (5G) network has attracted attention as the solution to address the increasing demand for mobile data communications. 5G has improved some areas that were not properly addressed in the fourthgeneration mobile communications (4G) network such as higher data rates, lower end-to-end (E2E) latency, higher reliability, and massive device connections [1]
WORK In this paper, we proposed an resource blocks (RBs) allocation method using Ape-X that is not affected by changes in the number of slices
We clarified that the proposed method almost completely satisfies the requirements set in the slice with the minimum RB allocation
Summary
5G has improved some areas that were not properly addressed in the fourthgeneration mobile communications (4G) network such as higher data rates, lower end-to-end (E2E) latency, higher reliability, and massive device connections [1]. Through these enhancements, 5G provides 4K and 8K resolution data utilizing high-speed and high-capacity communications, automatic operation and telemedicine services utilizing low-delay and reliable communications, and smart cities through massive connections. Services for Internet of Things devices require massive connections, but a high data rate is not important. VR services require a high data rate, but do not require massive connections. To deploy the NS concept, various network resources should be divided, and each resource
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