Risk-Aware Resource Management in Public Safety Networks.

Area of Science:

• Wireless Communication
• Network Engineering
• Public Safety

Background:

• Public Safety Networks (PSNs) increasingly utilize Unmanned Aerial Vehicles (UAVs) for resilient communication during catastrophic events.
• Mobile UAVs offer superior communication flexibility but risk over-exploitation due to interference.
• User decisions on power investment introduce uncertainty and risk.

Purpose of the Study:

• To propose a distributed, user-centric, risk-aware resource management framework for UAV-assisted PSNs.
• To model user risk-aware behavior using Prospect Theory for transmission power investment.
• To analyze non-cooperative game dynamics for optimizing power allocation between static and mobile UAVs.

Main Methods:

• Formulation of a non-cooperative game where users maximize their prospect-theoretic utility.
• Design of a user's prospect-theoretic utility function reflecting risk-averse behavior.
• Proof of existence and uniqueness of a Pure Nash Equilibrium (PNE) and development of a low-complexity algorithm for its determination.

Main Results:

• Demonstration of the framework's effectiveness in improving achievable data rates and spectrum utilization.
• Validation of the proposed framework's superiority over existing approaches through simulations.
• Confirmation of convergence to the Pure Nash Equilibrium.

Conclusions:

• The proposed framework effectively manages resources in UAV-assisted PSNs by considering user risk-aversion.
• The model provides a robust solution for optimizing spectrum utilization and communication reliability.
• The developed algorithm efficiently determines the optimal user strategies for enhanced network performance.