TY - GEN
T1 - Dynamic clustering algorithm design for ultra dense small cell networks in 5G
AU - Chen, Siyi
AU - Xing, Chengwen
AU - Fei, Zesong
AU - Wang, Hualei
AU - Pan, Zhengang
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/6/22
Y1 - 2016/6/22
N2 - Ultra dense networks are a promising technology enabling high power and spectrum efficiencies in future wireless systems. It is well-known that for ultra dense networks inter-cell interference is one of the main bottlenecks prohibiting achieving the promised performance gains. In order to effectively coordinate or mitigate interference in paper, we propose a graph-based low complexity dynamic clustering algorithm. The key idea behind the proposed algorithm is that dividing the whole network into a number of clusters under size constraint and the maximum intra-cluster interference and minimum inter-cluster interference. The logic is maximum intra-cluster can be effectively controlled by the coordination within each cluster. Meanwhile, graph-based algorithm is exploited to further reduce implementation complexity and make the proposed algorithm suitable for practical implementation. Finally, simulation results numerically demonstrate that the proposed low complexity algorithm has almost the same performance compared to the existing high performance algorithm but the complexity is much lower.
AB - Ultra dense networks are a promising technology enabling high power and spectrum efficiencies in future wireless systems. It is well-known that for ultra dense networks inter-cell interference is one of the main bottlenecks prohibiting achieving the promised performance gains. In order to effectively coordinate or mitigate interference in paper, we propose a graph-based low complexity dynamic clustering algorithm. The key idea behind the proposed algorithm is that dividing the whole network into a number of clusters under size constraint and the maximum intra-cluster interference and minimum inter-cluster interference. The logic is maximum intra-cluster can be effectively controlled by the coordination within each cluster. Meanwhile, graph-based algorithm is exploited to further reduce implementation complexity and make the proposed algorithm suitable for practical implementation. Finally, simulation results numerically demonstrate that the proposed low complexity algorithm has almost the same performance compared to the existing high performance algorithm but the complexity is much lower.
KW - Ultra dense networks
KW - clustering algorithms
KW - graph-based algorithm
UR - http://www.scopus.com/inward/record.url?scp=84980332038&partnerID=8YFLogxK
U2 - 10.1109/CHINACOM.2015.7498053
DO - 10.1109/CHINACOM.2015.7498053
M3 - Conference contribution
AN - SCOPUS:84980332038
T3 - Proceedings of the 2015 10th International Conference on Communications and Networking in China, CHINACOM 2015
SP - 836
EP - 840
BT - Proceedings of the 2015 10th International Conference on Communications and Networking in China, CHINACOM 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Communications and Networking in China, CHINACOM 2015
Y2 - 15 August 2015 through 17 August 2015
ER -