Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment.

Area of Science:

• Wireless Communication
• Network Engineering
• Game Theory

Background:

• Small cells enhance wireless network coverage and capacity, especially where traditional infrastructure is impractical.
• Unmanned aerial vehicles (UAVs) offer rapid deployment of small cells for aerial network access in challenging or remote locations.
• UAVs are increasingly used by telecom providers for rural, disaster-stricken, or event-based network coverage.

Purpose of the Study:

• To optimize energy consumption in UAV-deployed small cell networks.
• To investigate the strategic scheduling of beaconing periods for energy efficiency.
• To analyze the network's convergence to a stable operating point using game theory.

Main Methods:

• Formulated the problem as a sub-modular game.
• Investigated the structural properties of the game.
• Developed a learning algorithm for Nash equilibrium convergence.
• Conducted extensive numerical simulations.

Main Results:

• Demonstrated energy consumption optimization through strategic beaconing period scheduling.
• Achieved convergence to a Nash equilibrium operating point for the UAV network.
• Validated significant energy and data rate efficiency gains via the strategic beaconing policy.

Conclusions:

• Strategic beaconing period scheduling is an effective method for optimizing energy consumption in UAV-based small cell networks.
• The proposed learning algorithm ensures stable network operation at a Nash equilibrium.
• The findings support the practical deployment of UAVs for efficient aerial network access.