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A mobile edge computing offload decision-making method based on deep reinforcement learning

A technology that strengthens learning and decision-making methods, applied in the field of communication, to ensure the quality of service and minimize energy loss

Active Publication Date: 2021-06-18
杭州齐智能源科技股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When users offload their tasks to the base station or the cloud, they can reduce their own energy consumption, but the service quality of these offloaded tasks will be affected by some additional losses, such as transmission delay

Method used

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  • A mobile edge computing offload decision-making method based on deep reinforcement learning
  • A mobile edge computing offload decision-making method based on deep reinforcement learning
  • A mobile edge computing offload decision-making method based on deep reinforcement learning

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Embodiment Construction

[0063] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0064] refer to figure 1 and figure 2 , a mobile edge computing shunt decision-making method based on deep reinforcement learning, the implementation of this method can minimize the overall energy loss, transmission loss and delay loss, and ensure the quality of service. The present invention is based on a multi-user system model (such as figure 1 As shown), an offload decision method is proposed to determine which tasks of which users will be offloaded to the cloud. At the same time, if the task is selected to be offloaded, its uplink and downlink rates will also be optimized to achieve the minimum energy loss. The shunt decision-making method includes the following steps (such as figure 2 shown):

[0065] 1) In a mobile communication system consisting of multiple users, and each user has multiple independent tasks, x nm The splitting decision of task ...

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Abstract

A mobile edge computing shunt decision method based on deep reinforcement learning, including the following steps: 1) Calculate all the energy consumption in the mobile communication system under the given shunt decision; 2) When the user task is shunted, calculate the Delay loss in the process and processing; 3) Find an optimal split decision-making scheme through deep reinforcement learning algorithm; 4) Split decision x of all users nm And the uplink and downlink rates and the system state x as reinforcement learning t , and action a is the system state x t , if the total loss of the modified system is smaller than the previous one, then make the current reward r(x t , a) is set to a positive value, otherwise it is set to a negative value, and the system enters the next state x t+1 , repeating this iterative process until the best split decision x nm and uplink and downlink rates and the present invention minimizes energy consumption under the premise of ensuring user experience.

Description

technical field [0001] The invention belongs to the field of communication, and in particular relates to a communication system for mobile edge computing and a user task distribution decision method based on deep reinforcement learning for base station nodes. Background technique [0002] With the extensive development of wireless communication technology, wireless communication technology has penetrated into every aspect of human life. Mobile edge computing expands the capabilities of mobile devices, and with the help of abundant cloud resources, user experience is greatly improved. In a multi-user mobile communication system, all users share transmission resources. When users offload their own tasks to the base station or the cloud, they can reduce their own energy consumption, but the service quality of these offloaded tasks will be affected by some additional losses, such as transmission delay. In order to minimize all energy loss, transmission loss and delay loss, and...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W24/02H04W28/10H04W28/06
CPCH04W24/02H04W28/06H04W28/10Y02D30/70
Inventor 黄亮冯旭钱丽萍吴远
Owner 杭州齐智能源科技股份有限公司