The integration of the non-terrestrial network (NTN) and the terrestrial network (TN) aims to provide seamless and ubiquitous connectivity by using satellites, unmanned aerial vehicles, and high-altitude platforms to expand coverage in remote and underserved areas. Despite its potential, the integration of NTN and TN faces significant challenges in interference management and resource allocation, primarily due to the shared spectrum and the distinct operational characteristics of both network types. To address these issues, this paper proposes a reinforcement learning-based framework for dynamic resource allocation and interference management in integrated NTN-TN environments. The framework is designed to maintain balanced performance between NTN and TN while achieving a high signal-to-interference-plus-noise ratio (SINR) and coverage probability (CP). An interference approximation model, validated via Monte Carlo simulations, ensures reliability and accuracy across various scenarios. The simulation results demonstrate that the framework effectively mitigates interference, adapting to varying user equipment densities while minimizing performance degradation. The proposed approach significantly enhances the co-existence of NTN and TN, achieving higher SINR and CP compared to existing methods. These findings establish a robust foundation for scalable and efficient NTN-TN integration in next-generation networks.
