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5G Network Slicing Resource Allocation Method Based on Reinforcement Learning

A network slicing and resource allocation technology, applied in the field of 5G network slicing resource allocation based on reinforcement learning, can solve problems such as virtualization function limitations, and achieve the effect of efficient allocation

Active Publication Date: 2021-11-16
DONGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, to provide better performing and more cost-effective services, network slicing involves more challenging technical issues because (a) for radio access networks, spectrum is a scarce resource and it is necessary to guarantee spectral efficiency (SE). Significant, while for the core network, the virtualization function is also limited by computing resources; (b) the service level agreement (SLA) signed with the slice tenant usually imposes strict requirements on the quality of experience (QoE) perceived by the user; (c) Actual demand per slice depends largely on mobile user request patterns

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  • 5G Network Slicing Resource Allocation Method Based on Reinforcement Learning
  • 5G Network Slicing Resource Allocation Method Based on Reinforcement Learning
  • 5G Network Slicing Resource Allocation Method Based on Reinforcement Learning

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[0026] The present invention is further illustrated in conjunction with specific embodiments. It will be appreciated that these examples are intended to illustrate the scope of the invention and are not intended to limit the scope of the invention. It will be appreciated that after reading the present invention, those skilled in the art can make various modifications or modifications of the present invention, which also fall in the scope of the claims appended claims.

[0027] Embodiments of the present invention relate to a 5G network slice resource allocation method based on enhanced learning, including the following steps: Prediction of business traffic by considering the traffic flow rate in the future network slice, thereby inferring the division of future network resources Further, by enhancing the learning algorithm, the network resource division status of the future always affects the current divisional strategy, resulting in the current best policy to meet the efficient a...

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Abstract

The present invention relates to a 5G network slice resource allocation method based on enhanced learning, comprising the following steps: by considering the change of service flow in the future network slice, predicting the service flow, thereby inferring the division of future network resources; The enhanced learning algorithm makes the network resource division status in the future affect the current division strategy, so as to obtain the current best strategy, which can meet the needs of efficient allocation of 5G network resources.

Description

Technical field [0001] The present invention relates to a 5G network slice resource allocation method based on enhanced learning, which can be applied to the field of network resource allocation. By research on resources for resources on resources for 5G networks, an efficient resource allocation method is proposed to improve overall Resource utilization and user experience. Background technique [0002] In order to provide independent network services for various services without separately laying a private network, 5G network introduces network slice technology, which makes physical infrastructure resource virtualization into multiple independent parallel network slices, each Network slices serve a specific business scenario to meet different business scenarios on differentiated requirements such as bandwidth, delay, service quality, to meet various vertical industries diversified demand to enhance network elasticity and adaptability. Network slicing technology has enhanced net...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W24/02H04W28/16H04L12/24G06N3/04
CPCH04W24/02H04W28/16H04L41/0893H04L41/147H04L41/0823H04L41/0896G06N3/044G06N3/045
Inventor 肖苏超陈雯
Owner DONGHUA UNIV