IRS assisted hybrid HAP UAV uplink NOMA networks: an interference aware optimization framework.

Area of Science:

• Wireless Communication Networks
• Intelligent Reflecting Surfaces (IRS)
• Non-Orthogonal Multiple Access (NOMA)

Background:

• Dense uplink connectivity demands advanced solutions for efficient data offloading and monitoring.
• Hybrid aerial networks (HAP-UAV) offer potential but face challenges with interference and deployment optimization.
• Intelligent Reflecting Surfaces (IRS) can enhance wireless environments by intelligently manipulating signal propagation.

Purpose of the Study:

• To develop and optimize an IRS-assisted hybrid HAP-UAV uplink communication network using NOMA.
• To address the challenges of UAV deployment, user association, power allocation, and IRS phase shifts in a unified framework.
• To maximize the uplink sum-rate under practical constraints for applications like remote mining.

Main Methods:

• Formulation of a joint uplink sum-rate maximization problem as a non-convex mixed-integer optimization.
• Development of a low-complexity block coordinate descent (BCD)-based framework for iterative optimization.
• Simulation and performance evaluation against conventional benchmarks.

Main Results:

• The proposed IRS-assisted hybrid HAP-UAV NOMA system significantly outperforms existing schemes.
• Achieved uplink sum-rate improvements of up to 23.03% compared to non-IRS hybrid systems.
• Demonstrated substantial gains over UAV-only and HAP-only IRS-assisted schemes, with additional benefits from IRS-assisted user association.

Conclusions:

• The IRS-assisted hybrid HAP-UAV uplink NOMA network offers a scalable and reliable solution for future 6G dense communication.
• The BCD-based optimization framework effectively addresses complex system parameters for enhanced performance.
• This technology supports digital transformation initiatives, particularly in demanding environments like remote mining.