The increasing number of mobile devices and the enhanced user experience they require have a strong impact on mobile network development, since they result in an increased channel capacity demand to be obtained with a limited site densification. An interesting approach to face this challenge can be found in the combination of two technology enabling solutions: Cloud or Centralized RAN (C-RAN) and Advanced Antenna Systems (AAS). In this paper, we discuss the advantages given by these solutions, with a special focus on how Artificial Intelligence (AI)-based algorithms can improve their combination in terms of functional split and antenna mapping, stated as an optimization problem. In particular, AI can be beneficial in three main areas, such as the actual solution of the optimization problem, the tuning of parameters used in classical heuristic algorithms aiming at solving the optimization problem, and, finally, the traffic and resource allocation prediction at the base of proactive reconfiguration frameworks.

Perspectives on AI-based Algorithms Applied to C-RAN Functional Splitting and Advanced Antenna System Problem / Lavacca, F. G.; Eramo, V.; Cianfrani, A.; Listanti, M.; Valente, F.. - (2022), pp. 1-5. (Intervento presentato al convegno 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022 tenutosi a Budapest; Hungary) [10.1109/NOMS54207.2022.9789714].

Perspectives on AI-based Algorithms Applied to C-RAN Functional Splitting and Advanced Antenna System Problem

Lavacca F. G.
;
Eramo V.;Cianfrani A.;Listanti M.;Valente F.
2022

Abstract

The increasing number of mobile devices and the enhanced user experience they require have a strong impact on mobile network development, since they result in an increased channel capacity demand to be obtained with a limited site densification. An interesting approach to face this challenge can be found in the combination of two technology enabling solutions: Cloud or Centralized RAN (C-RAN) and Advanced Antenna Systems (AAS). In this paper, we discuss the advantages given by these solutions, with a special focus on how Artificial Intelligence (AI)-based algorithms can improve their combination in terms of functional split and antenna mapping, stated as an optimization problem. In particular, AI can be beneficial in three main areas, such as the actual solution of the optimization problem, the tuning of parameters used in classical heuristic algorithms aiming at solving the optimization problem, and, finally, the traffic and resource allocation prediction at the base of proactive reconfiguration frameworks.
2022
2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022
advanced antennas; c-ran; functional splitting
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Perspectives on AI-based Algorithms Applied to C-RAN Functional Splitting and Advanced Antenna System Problem / Lavacca, F. G.; Eramo, V.; Cianfrani, A.; Listanti, M.; Valente, F.. - (2022), pp. 1-5. (Intervento presentato al convegno 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022 tenutosi a Budapest; Hungary) [10.1109/NOMS54207.2022.9789714].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1651010
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