Calculation unloading and resource allocation method and device based on Q learning

A resource allocation and computing offloading technology, applied in the field of computing offloading and resource allocation based on Q-learning, can solve problems such as high UE energy consumption, and achieve the effect of reducing energy consumption

Active Publication Date: 2020-07-10
CHINA THREE GORGES UNIV
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Problems solved by technology

[0004]Existing solutions only focus on the performance of quasi-static systems, and ignore the impact of different resource requirements and limited resourc

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  • Calculation unloading and resource allocation method and device based on Q learning
  • Calculation unloading and resource allocation method and device based on Q learning
  • Calculation unloading and resource allocation method and device based on Q learning

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Embodiment Construction

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.

[0038] With the emergence of many emerging wireless services in 5G networks, mobile applications, especially more and more computing-intensive tasks, such as online interactive games, face recognition, and augmented / virtual reality (AR / VR), have led to The unprecedented explosive growth of data traffic. Generally, these emerging applicatio...

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Abstract

The embodiment of the invention provides a dynamic calculation unloading and resource allocation method and device based on Q learning, and the method comprises the steps: calculating the total calculation resources of an MEC server based on the calculation task parameters of UE, the performance parameters of the UE, the channel parameters between the UE and an AP, and the mobile edge, and constructing an optimization problem model; and determining the optimal solution of the optimization problem model based on Q learning, wherein the optimal solution comprises an unloading decision of the UE,a percentage of computing resources allocated to the UE by the MEC server, and a percentage of spectrum resources allocated to the UE by the AP. According to the calculation unloading and resource allocation method and device based on Q learning provided by the embodiment of the invention, and meanwhile, the actual calculation unloading and resource allocation characteristics in the time-varyingMEC system are considered, the delay threshold of the task and the limited resource capacity constraint of the system, and the joint optimal scheme of calculation unloading and resource allocation based on Q learning is determined, thereby further reducing the energy consumption of the UE.

Description

technical field [0001] The present invention relates to the technical field of mobile communication, in particular to a method and device for computing offloading and resource allocation based on Q-learning. Background technique [0002] In order to alleviate the increasingly severe conflict between application requirements and resource-constrained user equipment (UE), considering that the computing power and storage capacity of cloud servers deployed in mobile cloud computing (Mobile Cloud Computing, MCC) are obviously higher than UE, which prompts the emergence of MCC as an effective solution. However, MCC technology inevitably faces the problem that the deployed cloud server is far away from the user equipment, which may cause additional transmission energy overhead when the user equipment transmits data to the cloud server. In addition, long-distance transmission cannot guarantee the Quality of Service (QoS) of delay-sensitive applications. [0003] In the prior art, a...

Claims

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Application Information

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IPC IPC(8): H04W16/10H04W16/22
CPCH04W16/10H04W16/22Y02D30/70
Inventor 周欢江恺冯阳
Owner CHINA THREE GORGES UNIV
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