# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
###### Abstract  
The integration of reconfigurable intelligent surfaces (RISs) with non-terrestrial networks (NTNs), particularly those enabled by unmanned aerial vehicles (UAVs) or drone-based platforms, has emerged as a transformative approach to enhance 6G connectivity in high-mobility scenarios. UAV-assisted NTNs offer flexible deployment, dynamic altitude control, and rapid network reconfiguration, making them ideal candidates for RIS-based signal optimization. However, the high mobility of UAVs and their three-dimensional trajectory dynamics introduce unique challenges in maintaining robust, low-latency links and seamless handovers. This paper presents a comprehensive performance analysis of RIS-assisted UAV-based NTNs, focusing on optimizing RIS phase shifts to maximize the signal-to-interference-plus-noise ratio (SINR), throughput, energy efficiency, and reliability under UAV mobility constraints. A joint optimization framework is proposed that accounts for UAV path loss, aerial shadowing, interference, and user mobility patterns, tailored specifically for aerial communication networks. Extensive simulations are conducted across various UAV operation scenarios, including urban air corridors, rural surveillance routes, drone swarms, emergency response, and aerial delivery systems. The results reveal that RIS deployment significantly enhances the SINR and throughput while navigating energy and latency trade-offs in real time. These findings offer vital insights for deploying RIS-enhanced aerial networks in 6G, supporting mission-critical drone applications and next-generation autonomous systems. reconfigurable intelligent surfaces (RISs); non-terrestrial networks (NTNs); 6G networks; drone communication systems; unmanned aerial vehicles +
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coverage, steering beams dynamically and compensating for channel impairments inherent in UAV-based NTNs. RISs are made up of programmable passive elements, and they can address the aforementioned limitations by dynamically reshaping electromagnetic wave propagation through real-time adjustment of reflection coefficients, thereby enhancing signal integrity, extending coverage, and suppressing interference [4]. Recent innovations integrate RISs with non-orthogonal multiple access (NOMA), which enables spectral efficiency gains by multiplexing users within shared resources. However, interference management and real-time coordination challenges remain unresolved [5]. Later, space-time coding metasurfaces were introduced to enable adaptive beamforming and joint data modulation, resulting in reliable connectivity in mobility scenarios such as airborne or satellite-to-ground communications [6]. Recently, AI-driven frameworks have been used to optimize RIS configurations to align with fluctuating channel states and energy constraints, thus maximizing spectral and energy efficiency in resource-constrained NTN deployments [7]. These developments are augmented by intelligent resource management protocols, such as BLASTER, which holistically coordinate bandwidth allocation, power distribution, and user-RIS association to harmonize quality of service (QoS) with operational sustainability in hybrid terrestrial NTN ecosystems [8]. RIS deployment demonstrably increases system-level metrics, including SINR, throughput, and BER, by mitigating path loss and multipath fading, critical for seamless global connectivity [9]. Despite these developments, there are practical barriers, such as computational complexity in real-time RIS reconfiguration, hardware limitations in extreme mobility scenarios, and scalability in multi-RIS architectures. Expanding RIS applications to emerging NTN use cases, such as urban air mobility, intelligent reflecting satellite arrays, and integrated space-air-ground networks, as shown in Figure 1, will be pivotal in realizing scalable and adaptive 6G infrastructures capable of supporting next-generation mobility and connectivity demands [10].