  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.
