Power-Efficient Wireless Coverage Using Minimum Number of UAVs.

Area of Science:

• Engineering
• Computer Science
• Telecommunications

Background:

• Natural disasters frequently cause cellular outages, necessitating backup communication solutions.
• Unmanned Aerial Vehicles (UAVs) offer a flexible and rapid deployment option for aerial base stations during emergencies.
• Efficient 3D deployment and power management are critical for maximizing UAV effectiveness in providing wireless coverage.

Purpose of the Study:

• To propose a multi-UAV 3D deployment strategy for cellular outage scenarios.
• To minimize the number of UAVs and their transmit power while ensuring user data rate requirements.
• To achieve maximum wireless coverage for both outdoor and indoor users.

Main Methods:

• An iterative algorithm combining clustering and 3D placement for UAVs.
• Utilized Particle Swarm Optimization (PSO) for both clustering and UAV placement.
• Evaluated performance against K-means clustering and Genetic Algorithm (GA)/Artificial Bees Colony (ABC) based placement algorithms.

Main Results:

• The PSO-based clustering algorithm required fewer UAVs compared to K-means.
• The proposed iterative PSO algorithm significantly reduced execution time compared to GA and ABC.
• Achieved 100% user coverage in uniform distribution scenarios, outperforming Circle Packing Theory (CPT) in coverage density.

Conclusions:

• The proposed multi-UAV 3D deployment strategy is effective for disaster scenarios.
• PSO-based optimization offers superior performance in terms of UAV count, power, and execution time.
• This approach enhances the reliability and efficiency of aerial base station deployment.