Super-dense edge computing network mobility management method based on deep reinforcement learning

Abstract

The invention discloses a super-dense edge computing network mobility management method based on deep reinforcement learning. The super-dense edge computing network mobility management method includesthe steps: establishing a communication time delay model, a computing model, a QoS model and a service cost migration model according to environment information and processing resource information; establishing a mobile management model according to the established model information, simplifying the problem by adopting a dynamic loss queue technology and a Lyapunov optimization method, and abstractly describing a dynamic change process of an ultra-dense edge computing environment of the mobile management model by adopting a discrete time Markov decision process; and establishing an algorithmbased on deep reinforcement learning according to the abstract model and obtaining an optimal mobility management decision. According to the super-dense edge computing network mobility management method, for a super-dense edge computing network, the mobility management decision is small in limitation and good in mobility, and on the premise of considering the integrity, dynamics and balance of thesystem, the optimal decision of the association network and task allocation in the user moving process is realized.

Description

technical field [0001] The invention belongs to the technical field, and in particular relates to a mobility management method for an ultra-dense edge computing network based on deep reinforcement learning. Background technique [0002] With the rapid development of smart mobile devices and the rise of technologies such as 5G and the Internet of Things, the demand for wireless connections and traffic is increasing. Today's network architecture is difficult to support the hundreds of times the demand for wireless connections and the wireless traffic that will increase by a hundred times in the future. . The increase of wireless traffic demand puts forward new requirements for wireless network capacity, and the ultra-dense network (UDN) technology emerges under such circumstances. UDN technology achieves a 100-fold increase in wireless network capacity by densely deploying small base stations in hotspots to cope with the increasing demand for mobile data traffic and wireless ...

AI Technical Summary

Problems solved by technology

Although the envisioned ultra-dense edge computing technology can improve the communication and computing capacity of the edge network, the problem of mobility management, that is, the problem of network switching, is often encountered in the implementation process.

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