Joint UAVs' Load Balancing and UEs' Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in...

Area of Science:

• Wireless Communications
• Network Engineering
• Robotics

Background:

• Unmanned Aerial Vehicles (UAVs) are increasingly utilized as mobile base stations (BSs) for emergency communications in 5G/6G networks.
• Ensuring load balancing among UAVs and data rate fairness for user equipments (UEs) are critical challenges in multi-UAV communication systems.
• Existing UAV deployment strategies often struggle to address these issues concurrently.

Purpose of the Study:

• To develop a joint optimization strategy for UAV deployment that simultaneously addresses UAV load balancing and UE data rate fairness.
• To propose a novel algorithm for optimizing UAV deployment in emergency communication scenarios.
• To enhance the performance of multi-UAV communication networks.

Main Methods:

• A UAV diffusion deployment algorithm based on the virtual force field method is proposed.
• Two virtual forces, UAV-UAV force (FU) and UE-UAV force (FV), are defined to represent load balancing and data rate fairness.
• A diffusion control strategy and successive convex optimization are employed to update these forces and control UAV motion.

Main Results:

• The proposed algorithm effectively optimizes both UAV load balancing and UE data rate fairness.
• Simulation results demonstrate superior performance compared to baseline algorithms.
• The virtual force field approach enables distributed optimization for global load balancing.

Conclusions:

• The developed UAV diffusion deployment algorithm successfully achieves simultaneous optimization of load balancing and data rate fairness.
• The virtual force field method provides an effective framework for addressing complex multi-UAV network challenges.
• This approach offers a promising solution for enhancing emergency communication network efficiency and reliability.