An RL approach to radio resource management in heterogeneous virtual RANs

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5G networks are primarily designed to support a wide range of services characterized by diverse key performance indicators (KPIs). A fundamental component of 5G networks, and a pivotal factor to the fulfillment of the services KPIs, is the virtual radio access network (RAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of virtual RANs in non-stationary environments, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the non-trivial interdependence of network and channel conditions. In this paper, we propose CAREM, an RL framework for dynamic radio resource allocation, which selects the best link and modulation and coding scheme (MCS) for packet transmission, so as to meet the KPI requirements in heterogeneous virtual RANs. To show its effectiveness in real-world conditions, we provide a proof-of-concept through actual testbed implementation. Experimental results demonstrate that CAREM enables an efficient radio resource allocation, for any of the considered time periodicity of the decision-making process.
Proceedings of: 2021 16th Annual Conference on Wireless On-demand Network Systems and Services Conference (WONS), 9-11 March 2021, Klosters, Switzerland.
5G technology, Reinforcement learning, Virtual RAN, Radio resource allocation, Heterogeneous networks
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Tripathi, S., Puligheddu, C. & Chiasserini, C. F. (9-11 March 2021). An RL Approach to Radio Resource Management in Heterogeneous Virtual RANs [proceedings]. 2021 16th Annual Conference on Wireless On-demand Network Systems and Services Conference (WONS), Klosters, Switzerland.