Interference-aware optimization of three-tier RIS-enhanced hierarchical aerial computing: integrating terrestrial...

Area of Science:

• Wireless communication networks
• Edge computing architectures
• Internet of Things (IoT) systems

Background:

• Emerging 6G networks require high-throughput, low-latency computing for IoT devices.
• Existing two-tier UAV-HAP systems face limitations in battery life, computational power, and co-channel interference (CCI).

Purpose of the Study:

• To propose a novel three-tier RIS-enhanced hierarchical aerial computing architecture for persistent, interference-managed 6G IoT coverage.
• To address the challenges of limited endurance, computational capacity, and CCI in current aerial frameworks.

Main Methods:

• A three-tier architecture integrating a base station with RIS (BS-RIS), RIS-equipped UAVs, and a stratospheric HAP.
• A sub-array RIS partitioning mechanism for inter-platform interference suppression (85%).
• A comprehensive signal-to-interference-plus-noise ratio (SINR) model and a three-stage optimization process (matching, phase optimization, task distribution).

Main Results:

• The proposed framework achieves approximately [Formula: see text] higher total computed data volume.
• Demonstrates [Formula: see text] higher task completion rate.
• Achieves [Formula: see text] lower average end-to-end delay compared to two-tier UAV-HAP systems.

Conclusions:

• The novel three-tier RIS-enhanced architecture effectively manages interference and enhances performance for 6G IoT networks.
• The proposed optimization techniques significantly improve data volume, task completion, and reduce latency.
• This framework offers a promising solution for persistent and efficient aerial computing in future wireless systems.