Open AccessSDN ‐Based Hierarchical Agglomerative Clustering Algorithm for Interference Mitigation in Ultra‐Dense Small Cell Networks
Author(s) -
Yang Guang,
Cao Yewen,
Esmailpour Amir,
Wang Deqiang
Publication year - 2018
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2017-0084
Subject(s) - cluster analysis , computer science , hierarchical clustering , base station , quality of service , interference (communication) , spectral efficiency , software defined networking , hierarchical clustering of networks , computer network , distributed computing , artificial intelligence , correlation clustering , canopy clustering algorithm , channel (broadcasting)
Ultra‐dense small cell networks ( UD ‐ SCN s) have been identified as a promising scheme for next‐generation wireless networks capable of meeting the ever‐increasing demand for higher transmission rates and better quality of service. However, UD ‐ SCN s will inevitably suffer from severe interference among the small cell base stations, which will lower their spectral efficiency. In this paper, we propose a software‐defined networking ( SDN )‐based hierarchical agglomerative clustering ( SDN ‐ HAC ) framework, which leverages SDN to centrally control all sub‐channels in the network, and decides on cluster merging using a similarity criterion based on a suitability function. We evaluate the proposed algorithm through simulation. The obtained results show that the proposed algorithm performs well and improves system payoff by 18.19% and 436.34% when compared with the traditional network architecture algorithms and non‐cooperative scenarios, respectively.