Resource allocation method based on multi-agent reinforcement learning in mobile edge computing system

A reinforcement learning and multi-agent technology, applied in the field of resource allocation based on multi-agent reinforcement learning, can solve the problem of limited resources such as MEC server bandwidth, and achieve the effects of reducing learning time, maximizing utility, and reducing costs

Active Publication Date: 2019-11-05
SOUTHEAST UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Due to the limited computing, storage, bandwidth and other resources of the MEC

Method used

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  • Resource allocation method based on multi-agent reinforcement learning in mobile edge computing system
  • Resource allocation method based on multi-agent reinforcement learning in mobile edge computing system
  • Resource allocation method based on multi-agent reinforcement learning in mobile edge computing system

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Embodiment Construction

[0038] The present invention is based on multi-agent reinforcement learning, makes full use of limited computing resources in the mobile edge cloud server, and maximizes the utility function of the terminal user under the premise that terminal task offloading is necessary. The implementation method of the present invention will be further described below in conjunction with the accompanying drawings.

[0039] Such as figure 1As shown, considering that there are a total of N user mobile terminals in the mobile edge system, the user set can be expressed as N={1,2,3,…,N}, and each user has computationally intensive tasks that need to be offloaded to the cloud server , divide the wireless channel into K subcarriers, set the wireless channel set K={1,2,3,...,K}, when the nth user selects the kth channel, on the contrary Multiple users can select the same channel at the same time, but a user can only select one channel at a time, that is,

[0040]

[0041] Since many users s...

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Abstract

The invention discloses a resource allocation method based on multi-agent reinforcement learning in a mobile edge computing system, which comprises the following steps: (1) dividing a wireless channelinto a plurality of subcarriers, wherein each user can only select one subcarrier; (2) enabling each user to randomly select a channel and computing resources, and then calculating time delay and energy consumption generated by user unloading; (3) comparing the time delay energy consumption generated by the local calculation of the user with the time delay energy consumption unloaded to the edgecloud, and judging whether the unloading is successful or not; (4) obtaining a reward value of the current unloading action through multi-agent reinforcement learning, and calculating a value function; (5) enabling the user to perform action selection according to the strategy function; and (6) changing the learning rate of the user to update the strategy to obtain an optimal action set. Based onvariable-rate multi-agent reinforcement learning, computing resources and wireless resources of the mobile edge server are fully utilized, and the maximum value of the utility function of each intelligent terminal is obtained while the necessity of user unloading is considered.

Description

technical field [0001] The present invention relates to mobile edge computing technology, in particular to a resource allocation method based on multi-agent reinforcement learning in a mobile edge cloud computing system (Mobile Edge Computing, MEC). Background technique [0002] With the development of the Internet, mobile smart terminals are becoming more and more popular, and their functions are becoming more and more powerful. New applications such as face recognition, image recognition, and augmented reality are emerging. However, these emerging applications require mobile devices to have certain computing resources, limited storage resources and battery capacity, and have high requirements for latency. Therefore, high-performance computing devices are required to replace smart terminals to complete computing tasks. Mobile Cloud Computing (MCC, Mobile Cloud Computing) can better meet its needs. [0003] Since it was proposed around 2005, cloud computing has greatly chan...

Claims

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Application Information

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IPC IPC(8): H04W72/04H04L29/08
CPCH04W72/04H04L67/10Y02D30/70
Inventor 夏玮玮张雅雯燕锋成华清胡静宋铁成沈连丰
Owner SOUTHEAST UNIV
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