Complementary Base Station Clustering for Cost-Effective and Energy-Efficient Cloud-RAN

Abstract : The fast growing mobile network data traffic poses great challenges for operators to increase their data processing capacity in base stations in an efficient manner. With the emergence of Cloud Radio Access Network (Cloud-RAN), the data processing units can now be centralized in a data center and shared among several base stations. By clustering base stations with complementary traffic patterns to the same data center, the deployment cost and energy consumption can be reduced. In this paper, we propose a two-phase framework to find optimal base station clustering schemes in a city-wide Cloud-RAN. First, we design a traffic profile for each base station, and propose an entropy-based metric to characterize the complementarity among base stations. Second, we build a graph model to represent the complementarity as link weight, and propose a distance-constrained clustering algorithm to find optimal base station clustering schemes. We evaluate the performance of our framework using two months of real-world mobile network traffic data in Milan, Italy. Results show that our framework effectively reduces 12.88% of deployment cost and 9.45% of energy consumption compared with traditional architectures, and outperforms the baseline method.
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https://hal.sorbonne-universite.fr/hal-01527851
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Longbiao Chen, Thi-Mai-Trang Nguyen, Gang Pan, Jérémie Jakubowicz, Linjin Liu, et al.. Complementary Base Station Clustering for Cost-Effective and Energy-Efficient Cloud-RAN. 14th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2017), Aug 2017, San Francisco, CA, United States. ⟨hal-01527851⟩

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