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Conference Papers Year : 2023

Reinforcement Learning based model for Maximizing Operator's Profit in Open-RAN

Mahdi Sharara
Sahar Hoteit
Véronique Vèque

Abstract

Open Radio Access Network (O-RAN) is a novel architecture that enables the disaggregation and the virtualization of network components. This would provide new ways to mix and match network components by "opening up" the interfaces between them. O-RAN enables driving down the costs of network deployments and allows the entry of new players into the RAN market. It enables network operators to maximize resource utilization and deliver new network edge services at a lower cost, resulting in higher profits for operators. In this context, we consider a computing resource allocation problem for maximizing the operator's profit. Given that an operator receives subscribers' payments and pays the infrastructure provider's costs, we model the problem using Mixed Integer Linear Programming (MILP). Then, we propose to solve the problem using Reinforcement Learning (RL). Our simulation results demonstrate the ability of the RL agent to increase the operator's profit while reducing the algorithmic complexity of the MILP solver.
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Dates and versions

hal-03981411 , version 1 (09-02-2023)

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Mahdi Sharara, Sahar Hoteit, Véronique Vèque. Reinforcement Learning based model for Maximizing Operator's Profit in Open-RAN. 2023 IEEE/IFIP Network Operations and Management Symposium, May 2023, Miami, United States. ⟨10.1109/NOMS56928.2023.10154452⟩. ⟨hal-03981411⟩
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