TY - GEN
T1 - Big-data-driven network partitioning for ultra-dense radio access networks
AU - Huang, Siqi
AU - Han, Tao
AU - Ansari, Nirwan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/28
Y1 - 2017/7/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85028296735&partnerID=8YFLogxK
U2 - 10.1109/ICC.2017.7996364
DO - 10.1109/ICC.2017.7996364
M3 - Conference Proceeding
AN - SCOPUS:85028296735
T3 - IEEE International Conference on Communications
BT - 2017 IEEE International Conference on Communications, ICC 2017
A2 - Debbah, Merouane
A2 - Gesbert, David
A2 - Mellouk, Abdelhamid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Communications, ICC 2017
Y2 - 21 May 2017 through 25 May 2017
ER -