An Adaptive Small Cell Clustering Method Based on Many-to-Many Matching

Abstract

The invention discloses a self-adaptive small cell clustering method based on multiple-to-multiple matching. The method is characterized by comprising the following steps: S1, generating a RSRP list according to the own received RSRP information by a user, and spontaneously reporting the RSRP list to the connected small cell at fixed time; S2, generating the own first priority user group for eachsmall cell based on multiple-to-one matching until traversing all users and the RSRP lists; S3, performing the self-adaptive small cell clustering based on a multiple-to-multiple matching algorithm until traversing the first priority user group; and S4, outputting a clustering matching result. Through the self-adaptive small cell clustering method based on multiple-to-multiple matching, the new technology adopted by the 5G scene is considered, the clustering result is stable, and the algorithm complexity is low.

Description

technical field [0001] The present invention relates to the field of wireless communication technology, in particular to small cell management and mobile communication technology in the ultra-dense small cell network (Ultra-Dense Small cell Network, UDSN) scenario in a 5G (5th-generation) mobile communication system. An Adaptive Small Cell Clustering Method for Many-to-Many Matching. Background technique [0002] Dense networking technology is one of the main key enabling technologies of 5G. It achieves seamless coverage by densely deploying low-power wireless access points (small cell base stations), improves the spatial reuse rate of wireless resources, and meets the ultra-high experience rate of users. and huge network capacity requirements. [0003] A typical UDSN is defined as the number of small cell base stations will far exceed the number of base stations in traditional cellular networks, reaching a level comparable to or even far exceeding the number of user equipm...

AI Technical Summary

Problems solved by technology

[0005] Most of the existing clustering schemes are based on the dense micro-base station scenario of the LTE system. Clustering is performed from the network side. The method is mainly a machine learning algorithm, which has high computational complexity and large fluctuations in the clustering results. Difficult to achieve

At the same time, under the development trend of multi-standard network integration, the cooperative scheduling between small cells and the irregular layout of small cell base stations lead to difficulties in cluster management.

For example, references Huang Z, Tian H, Qin C, et al. A Social-Energy Based Cluster Management Scheme for User-Centric Ultra-Dense Networks [J]. IEEE Access, 2017, 5(99): 10769-10781. The proposed AP (AccessPoint) clustering algorithm based on personalized recommendation has a complex mathematical model and is physically difficult to establish effectively. At the same time, the algorithm assumes that a single AP only serves one user at a time, and the algorithm is difficult to promote in practical applications.

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