A wireless resource scheduling method based on double-layer loop model

Abstract

The invention belongs to the field of wireless network active cognition, and particularly relates to a wireless resource scheduling method based on a double-layer loop model. The wireless resource scheduling method aims at actively achieving efficient wireless resource allocative ability, improving intelligence of a wireless network, and continuously improving user experience of a user in the network by operating of a double-layer cognition loop in the network. The wireless resource scheduling method includes that an upper layer decision-making loop and multiple lower layer execution loops are built, the upper layer decision-making loop guides the multiple lower layer execution loops to work, in the upper layer decision-making loop, data collection conducts, the collected data conducts judging, planning and decision making, then digging knowledge is stored into a knowledge base by learning, and after decision making, an obtained optimizing strategy is stored into a strategy base. In the lower layer execution loops, a real-time state of external network environment is collected, an operation condition is obtained by judging of the real-time state of the external network environment, and the optimizing strategy is obtained from the upper layer decision-making loop, and then configuration parameter in the optimizing strategy is outputted to a network entity.

Description

technical field [0001] The invention belongs to the field of wireless network active cognition, and in particular relates to a wireless resource scheduling method based on a double-layer loop model. Background technique [0002] Currently, the mobile Internet is evolving at an unprecedented speed. On the one hand, the distribution and structure of the mobile Internet are increasingly extensive and complex; on the other hand, its users put forward higher requirements for network services under the premise of the existing network environment and wireless resources. These requirements include: faster service feedback, more intelligent, independent, and even personalized service content. The emergence of high requirements has brought unprecedented challenges to traditional wireless resource scheduling. [0003] Traditional wireless resource scheduling methods are mainly passive and artificial. The main methods are: collect parameters and analyze data on the operating network, ...

AI Technical Summary

Problems solved by technology

However, the above two types of loop models are limited to a single radio entity

In fact, the core network and the access network have different scheduling capabilities for different network resources, and the difference in network location also causes differences in the active cognitive functions of the two layers of the network, so the two cannot be simply generalized. Cognitive loops must be designed separately

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