Resource allocation and unloading decision-making method based on multi-agent architecture reinforcement learning

Abstract

The invention relates to a resource allocation and unloading decision-making method based on multi-agent architecture reinforcement learning, and belongs to the technical field of mobile communication. According to the method, excitation constraints, energy constraints and network resource constraints are considered, wireless resource allocation, computing resource allocation and unloading decisions are jointly optimized, and a random optimization model for maximizing the QoE of a total user of a system is established and converted into an MDP problem. Secondly, according to the method, an original MDP problem is subjected to factorization, and a Markov game model is established; then, the method provides a centralized training and distributed execution mechanism based on an actor-evaluator algorithm. In the centralized training process, multiple agents obtain global information through cooperation, resource allocation and task unloading decision strategy optimization are achieved, andafter the training process is finished, all the agents independently conduct resource allocation and task unloading according to the current system state and strategy. According to the invention, theQoE of the user can be effectively improved, and the time delay and the energy consumption are reduced.

Description

technical field [0001] The invention belongs to the technical field of mobile communication, and relates to a resource allocation and unloading decision-making method based on multi-agent architecture reinforcement learning. Background technique [0002] With the development of network services, a large number of computing-intensive applications such as mobile shopping, face recognition, and augmented reality have received a lot of attention. These advanced applications require low latency. At the same time, due to the limitation of physical size, current IoT devices such as wearable devices have limited computing resources and battery capacity, which cannot well support the operation of advanced applications. Therefore, it cannot provide satisfactory QoE for users. In order to solve this problem, there have been a lot of researches on offloading all or part of the tasks to the resource-rich cloud center. However, the location where the cloud server is deployed is usually ...

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However, since D2D underlay communication may bring more severe co-channel interf

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