# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 2 Related Work  
The use of RISs has emerged as a pivotal strategy for manipulating electromagnetic waves to improve signal quality and spectral efficiency. [PERSON] et al. [16] introduce AIRIS, an approach that integrates AI into RIS-assisted signal processing, demonstrating notable improvements in handling complex channel conditions. Complementing this perspective, [PERSON] et al. [17] used pervasive machine learning frameworks that dynamically adjust RIS configurations to create smart radio environments, thus emphasizing the potential of learning-based strategies in mitigating the adverse effects of multipath reflections and interference. Furthermore, [PERSON] et al. [18] extend this concept to the security domain by employing transfer learning to enhance physical-layer security in aerial RIS-based networks, while [PERSON] et al. [19] push the boundaries of conventional RIS architectures by exploring \"beyond diagonal\" designs for 6G Internet of Things (IoT) applications. Although these works demonstrate significant advances in RIS design and optimization, they predominantly address terrestrial scenarios or isolated aspects of RIS operation without fully accounting for the dynamic and heterogeneous environments encountered in NTNs. In parallel with RIS research, NTNs have received considerable attention as a means to extend connectivity to areas where traditional infrastructure is insufficient or hard to reach. [PERSON] et al. [20] offer a comprehensive review of integrated terrestrial and non-terrestrial localization challenges in 6G, while [PERSON] et al. [21] provides a comprehensive review of NTN developments in 3 GPP Release-18, emphasizing adaptive beamforming and Doppler compensation for high-mobility users. The inherent challenges of high-mobility scenarios are further addressed in studies such as those by [PERSON] et al. [22] and [PERSON] et al. [23]. In addition, efficient path selection and integrated network architectures are proposed by [PERSON] et al. [24] to improve connectivity under dynamic conditions, prioritizing latency and reliability. The need for a robust handover scheme in rapidly changing environments is underscored by [PERSON] et al. [25] and [PERSON] et al. [26], whose investigations into adaptive handover management and low-Earth-orbit (LEO) satellite handover strategies provide critical insights for NTN deployments. Moreover, [PERSON] et al. [27] and [PERSON] and [PERSON] [28] highlight the role of AI and emerging technologies in converging the terrestrial and non-terrestrial domains. Despite these advances, most of the literature focuses on individual aspects of NTN performance, such as localization accuracy, path selection, and handover efficiency, leaving a gap in comprehensive studies that address the joint impact of RIS optimization in high-mobility NTN scenarios. Efforts to achieve a unified communication framework for 6G have also explored spectrum sharing, advanced antenna configurations, and edge computing. [PERSON] et al. [29] discuss innovative spectrum-sharing mechanisms in satellite-terrestrial integrated networks, while [PERSON] et al. [30] provide detailed work on massive MIMO techniques tailored for NTNs. [PERSON] et al. [31] introduce a multi-agent federated reinforcement learning approach for satellite edge computing, and [PERSON] et al. [32] examine the synergistic benefits of integrated environment sensing with green communication strategies in NTNs. These studies collectively underscore the importance of holistic system design; however, they tend to overlook the specific challenges associated with real-time RIS reconfiguration in high-mobility environments, which are critical when considering the diverse propagation characteristics and rapid channel variations typical of scenarios such as high-speed trains, aerial networks, and emergency communications, as shown in Table 1.