Power Control and Clustering-Based Interference Management for UAV-Assisted Networks.

Area of Science:

• Wireless Communication
• Network Engineering
• Game Theory

Background:

• Unmanned Aerial Vehicles (UAVs) are increasingly used in wireless networks for data collection and coverage.
• High density of line-of-sight (LoS) paths in air-to-ground channels leads to significant inter-UAV interference.
• Existing UAV network architectures face challenges in managing interference and optimizing performance.

Purpose of the Study:

• To develop an effective interference management framework for UAV-assisted wireless networks.
• To enhance system sum-rate and user data rates by mitigating inter-UAV interference.
• To investigate the application of game theory and clustering techniques for UAV network optimization.

Main Methods:

• Power control module modeled as a non-cooperative game, converging to a Nash equilibrium.
• Coordinated Multi-Point (CoMP) technique implemented to transform interfering links into useful ones.
• Affinity propagation algorithm used for UAV clustering based on interference strength.

Main Results:

• The integrated power control and CoMP framework significantly reduces interference compared to non-CoMP scenarios.
• System sum-rate is substantially improved by the proposed interference management strategy.
• Simulation results demonstrate the effectiveness of UAV clustering in optimizing network performance.

Conclusions:

• The proposed interference management framework, combining power control and CoMP, is effective for UAV networks.
• UAV clustering influences network performance metrics such as average cluster size and number of clusters.
• The study provides insights into optimizing UAV network deployment and operation for improved efficiency.