Big-data-driven network partitioning for ultra-dense radio access networks

Document Type

Conference Proceeding

Publication Date

7-28-2017

Abstract

The increased density of base stations (BSs) may significantly add complexity to network management mechanisms and hamper them from efficiently managing the network. In this paper, we propose a big-data-driven network partitioning and optimization framework to reduce the complexity of the networking mechanisms. The proposed framework divides the entire radio access network (RAN) into multiple sub-RANs and each sub-RAN can be managed independently. Therefore, the complexity of the network management can be reduced. Quantifying the relationships among BSs is challenging in the network partitioning. We propose to extract three networking features from mobile traffic data to discover the relationships. Based on these features, we engineer the network partitioning solution in three steps. First, we design a hierarchical clustering analysis (HCA) algorithm to divide the entire RAN into sub-RANs. Second, we implement a traffic load balancing algorithm to characterize the performance of the network partitioning. Third, we adapt the weights of networking features in the HCA algorithm to optimize the network partitioning. We validate the proposed solution through simulations designed based on real mobile network traffic data. The simulation results reveal the impacts of the RAN partitioning on the networking performance and the computational complexity of the networking mechanism.

Identifier

85028296735 (Scopus)

ISBN

[9781467389990]

Publication Title

IEEE International Conference on Communications

External Full Text Location

https://doi.org/10.1109/ICC.2017.7996364

ISSN

15503607

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