The virtualization of radio access networks (RANs) is emerging as a key component of future wireless systems, as it brings agility to the RAN architecture and offers degrees of design freedom. In this paper, we investigate and characterize the computational and memory requirements of virtual RANs. To this end, we build a virtual RAN test-bed leveraging the srsRAN open-source mobile communication platform and general-purpose processor-based servers. Through extensive experiments, we profile the consumption of computing and memory resources, and we assess the system performance. Further, we build regression models to predict the system behavior as the number of connected users increases, under diverse radio transmission settings. In so doing, we develop a methodology and prediction models that can help designing and optimizing virtual RANs.

Characterizing the Computational and Memory Requirements of Virtual RANs / Pramanik, Somreeta; Ksentini, Adlen; Chiasserini, Carla Fabiana. - ELETTRONICO. - (2022). (Intervento presentato al convegno IEEE/IFIP WONS 2022 tenutosi a Virtual conference due to COVID-19 nel 30 March 2022 - 01 April 2022) [10.23919/WONS54113.2022.9764455].

Characterizing the Computational and Memory Requirements of Virtual RANs

Somreeta Pramanik;Carla Fabiana Chiasserini
2022

Abstract

The virtualization of radio access networks (RANs) is emerging as a key component of future wireless systems, as it brings agility to the RAN architecture and offers degrees of design freedom. In this paper, we investigate and characterize the computational and memory requirements of virtual RANs. To this end, we build a virtual RAN test-bed leveraging the srsRAN open-source mobile communication platform and general-purpose processor-based servers. Through extensive experiments, we profile the consumption of computing and memory resources, and we assess the system performance. Further, we build regression models to predict the system behavior as the number of connected users increases, under diverse radio transmission settings. In so doing, we develop a methodology and prediction models that can help designing and optimizing virtual RANs.
2022
978-3-903176-46-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2954149