A Context-aware Radio Resource Management in Heterogeneous Virtual RANs

TL;DR

This paper introduces CAREM, a reinforcement learning framework for dynamic radio resource management in heterogeneous vRANs, significantly improving KPIs like packet loss and latency through adaptive control.

Contribution

It presents a novel reinforcement learning-based approach for context-aware resource allocation in vRANs, outperforming existing neural network and LTE schemes.

Findings

CAREM reduces packet loss and latency by an order of magnitude.

It achieves 65% latency improvement over contextual bandit methods.

Experimental results validate its effectiveness across various traffic demands.

Abstract

New-generation wireless networks are designed to support a wide range of services with diverse key performance indicators (KPIs) requirements. A fundamental component of such networks, and a pivotal factor to the fulfillment of the target KPIs, is the virtual radio access network (vRAN), which allows high flexibility on the control of the radio link. However, to fully exploit the potentiality of vRANs, an efficient mapping of the rapidly varying context to radio control decisions is not only essential, but also challenging owing to the interdependence of user traffic demand, channel conditions, and resource allocation. Here, we propose CAREM, a reinforcement learning framework for dynamic radio resource allocation in heterogeneous vRANs, which selects the best available link and transmission parameters for packet transfer, so as to meet the KPI requirements. To show its effectiveness, we develop a testbed for proof-of-concept. Experimental results demonstrate that CAREM enables an efficient radio resource allocation under different settings and traffic demand. Also, compared to the closest existing scheme based on neural network and the standard LTE, CAREM exhibits an improvement of one order of magnitude in packet loss and latency, while it provides a 65% latency improvement relatively to the contextual bandit approach.