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Hyperbolic K-means for traffic-aware clustering in cloud and virtualized RANs

Abstract : As the internet and connected objects gain more and more in popularity, serving the ever-increasing data traffic becomes a challenge for the mobile operators. The traditional cellular radio access network (RAN), where each base station is co-located with its own processing unit and is responsible for a specific geographic area, has evolved first with the so-called Cloud RAN (C-RAN), and is currently undergoing further architectural evolution under the virtualized RAN (vRAN), Open RAN (O-RAN) and Software-Defined RAN (SD-RAN) architectures. In all these versions, the data processing units can be dynamically centralized into a pool and shared between several base stations, enlarging the geographical view for scheduling and resource allocation algorithms. For instance, resource utilisation is improved by avoiding resource idling during off-peak hours. C-RAN and vRAN gains depend strongly on the clustering scheme of radio units (RRHs and RUs). In this paper, we propose a novel radio clustering algorithm that takes into account both the traffic demand and the position of stations, by using the hyperbolic distance in 3dimensions. We introduce a modified K-means clustering algorithm, called Hyperbolic K-means, and show that this generates geographically compact RU clusters with traffic charge equally shared among them. Application of our algorithm on real-world mobile data traffic, collected from the cities of Nantes and Lille in France, shows an increase in resource utilisation by 25%, and a reduction in deployment cost by 15%, compared to the standard RAN. Furthermore, the performance of our Hyperbolic K-means algorithm is compared extensively against alternative C-RAN clustering proposals from the literature and is shown to outperform them, in resource utilisation as well as in cost reduction.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-03109662
Contributor : Anastasios Giovanidis <>
Submitted on : Wednesday, January 13, 2021 - 9:00:24 PM
Last modification on : Saturday, February 13, 2021 - 3:29:06 AM

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  • HAL Id : hal-03109662, version 1

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Hanane Djeddal, Liticia Touzari, Anastasios Giovanidis, Chi-Dung Phung, Stefano Secci. Hyperbolic K-means for traffic-aware clustering in cloud and virtualized RANs. 2021. ⟨hal-03109662v1⟩

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