Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs

TL;DR

This paper introduces a deep reinforcement learning framework to optimize vRAN reconfigurations, reducing operational costs by up to 76% through adaptive, cost-aware network management strategies.

Contribution

It formulates a novel vRAN reconfiguration problem and develops a model-free RL solution using an action branching architecture with D3QN, addressing high-dimensional action spaces.

Findings

Achieves up to 76% cost reduction compared to non-adaptive methods.

Successfully learns optimal vRAN configurations in simulation and real testbed.

Transfer learning accelerates convergence in different vRAN systems.

Abstract

Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this paper, a novel vRAN reconfiguration problem is formulated to jointly reconfigure the functional splits of the base stations (BSs), locations of the virtualized central units (vCUs) and distributed units (vDUs), their resources, and the routing for each BS data flow. The objective is to minimize the long-term total network operation cost while adapting to the varying traffic demands and resource availability. Testbed measurements are performed to study the relationship between the traffic demands and computing resources, which reveals high variance and depends on the platform and its load. Consequently, finding the perfect model of the underlying system is non-trivial. Therefore, to solve the proposed problem, a deep reinforcement learning (RL)-based framework is proposed and developed using model-free RL approaches. Moreover, the problem consists of multiple BSs sharing the same resources, which results in a multi-dimensional discrete action space and leads to a combinatorial number of possible actions. To overcome this curse of dimensionality, action branching architecture, which is an action decomposition method with a shared decision module followed by neural network is combined with Dueling Double Deep Q-network (D3QN) algorithm. Simulations are carried out using an O-RAN compliant model and real traces of the testbed. Our numerical results show that the proposed framework successfully learns the optimal policy that adaptively selects the vRAN configurations, where its learning convergence can be further expedited through transfer learning even in different vRAN systems. It offers significant cost savings by up to 59\% of a static benchmark, 35\% of DDPG with discretization, and 76\% of non-branching D3QN.