Calculation unloading and resource allocation method and device based on deep reinforcement learning

Abstract

The invention provides a calculation unloading and resource allocation method and device based on deep reinforcement 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 anoptimization problem model; and determining the optimal solution of the optimization problem model based on deep reinforcement learning, determining the unloading decision of the UE, and respectivelyallocating the percentage of the computing resources and the percentage of the spectrum resources to the UE. According to the calculation unloading and resource allocation method and device based on deep reinforcement learning provided by the invention, the actual calculation unloading and resource allocation characteristics in the time-varying MEC system are considered, the time delay threshold of the task and the limited resource capacity constraint of the system are based on deep reinforcement learning, the DNN is used for effectively approximating a value function in reinforcement learningso as to determine a joint optimal scheme of calculation unloading and resource allocation, and the energy consumption of the UE is further reduced.

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 deep reinforcement 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...

AI Technical Summary

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 resource capacity on the performance of MEC systems. Practical network applications However, there is still a technical problem of excessive UE energy consumption

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