RL-Based Resource Allocation in SDN-Enabled 6G Networks

Ivan Radosavljević, Petar D. Bojović () and Živko Bojović
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Ivan Radosavljević: Faculty of Informatics and Computing, Singidunum University, 11000 Belgrade, Serbia
Petar D. Bojović: School of Computing, Department of Computer Engineering, Union University in Belgrade, 6/6 Knez Mihailova, 11000 Belgrade, Serbia
Živko Bojović: Faculty of Informatics and Computing, Singidunum University, 11000 Belgrade, Serbia

Future Internet, 2025, vol. 17, issue 11, 1-19

Abstract: Dynamic and efficient resource allocation is critical for Software-Defined Networking (SDN) enabled sixth-generation (6G) networks to ensure adaptability and optimized utilization of network resources. This paper proposes a reinforcement learning (RL)-based framework that integrates an actor–critic model with a modular SDN interface for fine-grained, queue-level bandwidth scheduling. The framework further incorporates a stochastic traffic generator for training and a virtualized multi-slice platform testbed for a realistic beyond-5G/6G evaluation. Experimental results show that the proposed RL model significantly outperforms a baseline forecasting model: it converges faster, showing notable improvements after 240 training epochs, achieves higher cumulative rewards, and reduces packet drops under dynamic traffic conditions. Moreover, the RL-based scheduling mechanism exhibits improved adaptability to traffic fluctuations, although both approaches face challenges under node outage conditions. These findings confirm that queue-level reinforcement learning enhances responsiveness and reliability in 6G networks, while also highlighting open challenges in fault-tolerant scheduling.

Keywords: reinforcement learning; Software-Defined Networking; 6G networks; resource allocation; queue-level scheduling; actor–critic (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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