Abstract:
Sixth generation (6G) networks, the next wave of wireless communication, are anticipated to link billions of Internet of Things (IoT) devices with reliable coverage, fber-like data rates, and energy-efficient operation. Achieving this vision is challenging because conventional terrestrial systems struggle with blockage, uneven load, and limited scalability. To overcome these issues, this thesis explores the integration of Reconfgurable Intelligent Surfaces (RIS) with an Airborne Non-Terrestrial Networks (NTN), alongside conventional Macro Base Stations (MBS) and Terahertz (THz) small cells. A scalable Monte Carlo simulation framework was developed, modeling user association, SINR coverage, Rate coverage, and Energy efficiency in a three-tier heterogeneous network. IoT devices were distributed via a Poisson Point Process (PPP), while RISs were modeled as low-overhead reflectors that enhance serving links and shift tier-level association. UAVs operated at 500 m altitude, while THz small cells exploited 0.3 THz bands with bandwidths up to 5 GHz. The results show that RIS reshapes association probabilities, reducing over-reliance on the MBS tier and enabling more users to connect to UAV and THz layers. SINR coverage improves significantly with RIS, even under high biasing toward THz or UAVs, where traditional deployments would otherwise fail. Rate coverage analysis demonstrates that RIS makes high-rate IoT applications feasible at THz frequencies, with bandwidth scaling and densification producing consistent gains. In terms of energy efficiency, RIS increases bits-per-Joule performance nearly linearly up to an optimal density, while also relaxing the traditional trade-off between coverage and efficiency. Overall, the findings confirm that RIS-assisted NTN frameworks are a practical and scalable solution for 6G networks. They provide clear deployment guidelines: apply UAV biasing for wide-area IoT coverage, adopt RIS to make THz tiers usable for high data rates, and balance RIS density to maximize energy efficiency. The proposed framework demonstrates how RIS and UAVs can jointly address the most pressing challenges of 6G networks, offering a way toward sustainable and intelligent global connectivity. Quantitative results demonstrate that the proposed RIS-assisted NTN framework improves SINR coverage by approximately 33%, enhances rate coverage at THz frequencies by around 28%, and increases overall energy efficiency by about 43% compared to non-RIS systems, confirming its scalability and practical relevance for future 6G deployments.
