Enabling rApp in 5G O-RAN: An Spectral Optimization (SO)rApp Use Case

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.