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Mobile edge computational shunt decision method based on deep reinforcement learning

A technique for reinforcement learning, decision-making methods, applied in the field of communication

Active Publication Date: 2018-10-09
杭州齐智科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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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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  • Mobile edge computational shunt decision method based on deep reinforcement learning
  • Mobile edge computational shunt decision method based on deep reinforcement learning
  • Mobile edge computational shunt decision 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

The invention discloses a mobile edge computational shunt decision method. The method comprises the following steps: 1) computing all energy loss in a mobile communications system under the giving ofa shunt decision; 2) computing delay loss in each of the transmission procedure and the processing procedure when the user task is shunted; 3) searching an optimal shunt decision scheme through the deep reinforcement learning algorithm; 4) taking the shunt decision xnm and uplink and downlink rate as shown in description of all users as the system state xt of the reinforcement learning, and takingthe action a as the change on the system state xt, setting the current award r(xt, a) as the positive value if the total loss of the improved system is smaller than before, otherwise, setting as thenegative value; enabling the system to enter the next state xt+1, continuously repeating the iteration process until obtaining the optimal shunt decision xnm and the uplink-downlink rate as shown in description. The energy loss is minimized in the premise of guaranteeing the 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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IPC IPC(8): H04W24/02H04W28/10H04W28/06
CPCH04W24/02H04W28/06H04W28/10Y02D30/70
Inventor 黄亮冯旭钱丽萍吴远
Owner 杭州齐智科技有限公司
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