Fifth Generation (5G) introduces technologies that expedite the adoption of mobile networks, such as densely connected devices, ultra-fast data rate, low latency and more. With those visions in 5G and 6G in the next step, the need for a higher transmission rate and lower latency is more demanding, possibly breaking Moore’s law. With Artificial Intelligence (AI) techniques becoming mature in the past decade, optimizing resource allocation in the network has become a highly demanding problem for Mobile Network Operators (MNOs) to provide better Quality of Service (QoS) with less cost.
This thesis proposes a Reinforcement Learning (RL) solution on bandwidth allocation for network slicing integration in disaggregated Open Radio Access Network (O-RAN) architecture. O-RAN redefines traditional Radio Access Network (RAN) elements into smaller components with detailed functional specifications. The concept of open modularization leads to greater potential for managing resources of different network slices. In 5G mobile networks, there are three major types of network slices, Enhanced Mobile
Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC). Each network slice has different features in the 5G network; therefore, the resources can be relocated depending on different needs. The virtualization of O-RAN divides the RAN into smaller function groups. This helps the network slices to divide the shared resources further down.
Compared to traditional sequential signal processing, allocating dedicated resources for each network slice can improve the performance individually. In addition, shared resources can be customized statically based on the feature requirement of each slice. To further enhance the bandwidth utilization on the disaggregated O-RAN, a RL algorithm is proposed in this thesis on midhaul bandwidth allocation shared between Centralized Unit (CU) and Distributed Unit (DU).
A Python-based simulator has been implemented considering several types of mobile User Equipment (UE)s for this thesis. The simulator is later integrated with the proposed Q-learning model. The RL model finds the optimization on bandwidth allocation in midhaul between Edge Open Cloud (O-Cloud)s (DUs) and Regional O-Cloud (CU). The results show up to 50% improvement in the throughput of the targeted slice, fairness to other slices, and overall bandwidth utilization on the O-Clouds. In addition, the UE QoS has a significant improvement in terms of transmission time.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/45122 |
| Date | 06 July 2023 |
| Creators | Cheng, Nien Fang |
| Contributors | Erol-Kantarci, Melike |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Attribution-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-sa/4.0/ |
Page generated in 0.002 seconds