Radio Resource Management for Intelligent Neutral Host (INH) in Multi-Operator Environments

Abstract

In the era of fifth-generation (5G) cellular networks and beyond, network sharing has emerged as a promising approach to address the escalating demand for spectrum and infrastructure resources. Intelligent Neutral Host (INH) is an advanced network-sharing method facilitated by Open Radio Access Network (O-RAN) capabilities. This paper addresses the challenge of Radio Resource Management (RRM) in a multi-operator, multi-slice scenario. We propose an algorithm based on Q-learning and deep Q-learning, particularly concerning different Physical Resource Block (PRB) types to cater to diverse operator requirements. Implemented as an xApp on the Colosseum platform, our algorithm introduces a dynamic resource allocation strategy that adheres to Service Level Agreement (SLA) constraints and optimizes real-time Key Performance metrics (KPMs), including throughput, buffer occupancy, and PRB utilization. We assess the performance and efficacy of our algorithm in a complex traffic scenario to demonstrate how it effectively allocates resources among operators’ slices to satisfy their respective SLA while ensuring optimal resource utilization. The experimental results show that our proposed algorithm can efficiently allocate resources to individual slices while satisfying the SLA. Compared to traditional algorithms, our approach significantly minimizes SLA violations, reducing them to 2.5% for enhanced Mobile Broadband (eMBB) slices and eliminating them entirely for Ultra-Reliable Low-Latency Communications (URLLC) slices.

Keywords

• Intelligent neutral host
• multi-operator core network
• reinforcement learning
• deep Q-learning
• Q-learning
• service level agreement