Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul

TL;DR

This paper presents a reinforcement learning approach for dynamic resource allocation in O-RAN network slices, significantly improving throughput and transmission times for different service types like eMBB and URLLC.

Contribution

It introduces an RL-based resource allocation method tailored for O-RAN slicing, demonstrating improved performance through a simplified edge network simulator.

Findings

RL model enhances peak rate and transmission time for URLLC.

Dynamic bandwidth allocation benefits end-user data rates.

Simulation results outperform baseline resource management strategies.

Abstract

Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB). Open Radio Access Network (O-RAN), proposes an open and disaggregated concept of RAN by modulizing the functionalities into independent components. Network slicing for O-RAN can significantly improve performance. Therefore, an advanced resource allocation solution for network slicing in O-RAN is proposed in this study by applying Reinforcement Learning (RL). This research demonstrates an RL compatible simplified edge network simulator with three components, user equipment(UE), Edge O-Cloud, and Regional O-Cloud. This simulator is later used to discover how to improve throughput for targeted network slice(s) by dynamically allocating unused bandwidth from other slices. Increasing the throughput for certain network slicing can also benefit the end users with a higher average data rate, peak rate, or shorter transmission time. The results show that the RL model can provide eMBB traffic with a high peak rate and shorter transmission time for URLLC compared to balanced and eMBB focus baselines.