Phase shift optimization in reconfigurable intelligent surface-assisted UAV in hierarchical aerial computing networks

Area of Science:

• Wireless Communication
• Network Engineering
• Computer Science

Background:

• The 6G wireless network evolution demands advanced solutions for massive Internet of Things (IoT) deployments.
• Conventional terrestrial infrastructure faces limitations in meeting the unprecedented computational and communication requirements of future IoT.

Purpose of the Study:

• To develop a comprehensive framework integrating Reconfigurable Intelligent Surface (RIS) technology with hierarchical aerial computing networks.
• To address the limitations of terrestrial infrastructure by creating a three-tier computing hierarchy using RIS-equipped Unmanned Aerial Vehicles (UAVs) and High-Altitude Platforms (HAPs).

Main Methods:

• A system model combining RIS-equipped UAVs as mobile edge computing nodes with a High-Altitude Platform (HAP) for high-capacity computation.
• RIS phase optimization formulated as a Riemannian conjugate gradient problem on complex circle manifolds.
• A three-stage sequential decomposition approach for optimizing RIS phase configuration.

Main Results:

• The RIS-enhanced system achieved an 18% throughput improvement compared to systems without RIS.
• Near-linear scalability was demonstrated, serving approximately 100% of available IoT devices.
• A 95% task completion rate was maintained across all network loads, outperforming comparable algorithms.

Conclusions:

• RIS-enabled aerial networks offer a transformative solution for scalable and efficient 6G IoT services.
• Intelligent phase configuration enhances channel quality, improving resource utilization and service provisioning.
• The framework demonstrates practical viability for future multi-UAV scenarios and energy-efficient designs.