Enabling rApp in 5G O-RAN: An Spectral Optimization (SO)rApp Use Case
| dc.contributor.author | Mallu, Jaswanth Sai Reddy | en |
| dc.contributor.committeechair | Pereira da Silva, Luiz Antonio | en |
| dc.contributor.committeechair | Pereira da Silva, Aloizio | en |
| dc.contributor.committeemember | Stavrou, Angelos | en |
| dc.contributor.committeemember | Hammad, Eman | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2024-06-13T08:01:21Z | en |
| dc.date.available | 2024-06-13T08:01:21Z | en |
| dc.date.issued | 2024-06-12 | en |
| dc.description.abstract | This thesis comprehensively examines the rApp lifecycle within the O-RAN Alliance (O- RAN) Non-Real Time RIC (Non-RT RIC) framework, serving as a practical guide for exper- imental research and development. The focus is on the entire lifecycle of rApp development, from designing and onboarding to deployment and execution, using a spectral efficiency op- timization use case to illustrate the process. The study develops and integrates a Spectrum Optimization (SO)rApp employing Reinforcement Learning (RL) techniques, specifically a Deep Q-Network (DQN) model, within the O-RAN architecture. The research highlights how the SOrApp dynamically allocates spectrum resources to enhance network performance under varying demand conditions. Utilizing the Network Simulator (NS)-3 5G-LENA simulator, the thesis replicates diverse service demand scenarios to evaluate the rApp's effectiveness in optimizing spectral efficiency. The findings demonstrate that integrating Artificial Intelligence (AI)-driven rApps within the O-RAN framework significantly improves spectral efficiency and overall network performance, providing valuable insights and methodologies for future research and practical implementations in 5G networking. | en |
| dc.description.abstractgeneral | As 5G networks grow more advanced, managing them effectively becomes increasingly chal- lenging. This thesis explores a method to improve network performance using specialized software applications, known as rApp, within the O-RAN Non-RT RIC framework. By focusing on the lifecycle of these rApps—how they are created, onboarded, deployed, and executed. To illustrate this process, used a real-world example of optimizing the efficiency of spectrum use, which is crucial for maintaining high-speed data and reliable communication in 5G networks. Our findings show that integrating intelligent rApps driven by Machine Learning (ML) can significantly enhance the performance and efficiency of 5G networks, offering valuable insights for future innovations in this rapidly evolving field. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:40870 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/119415 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | O-RAN | en |
| dc.subject | rApp | en |
| dc.subject | 5G | en |
| dc.subject | ML | en |
| dc.title | Enabling rApp in 5G O-RAN: An Spectral Optimization (SO)rApp Use Case | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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