Joint Clustering and Power Optimization for the SPMA Protocol in UAV Swarm Communication in Frequency-Constrained...

Area of Science:

• Wireless Communication
• Network Optimization
• Swarm Intelligence

Background:

• Unmanned Aerial Vehicle (UAV) swarms experience performance issues on single-frequency channels due to the Statistical Priority-based Multiple Access (SPMA) protocol's contention conflicts.
• Limited frequency resources exacerbate these conflicts, hindering reliable communication in UAV swarms.

Purpose of the Study:

• To propose a joint clustering and power optimization method for the SPMA protocol in frequency-constrained scenarios.
• To enhance the end-to-end transmission success rate and reduce power consumption in UAV swarms.

Main Methods:

• A utility function was constructed based on the end-to-end transmission success rate.
• A three-stage heuristic algorithm was designed, incorporating K-means for clustering, GPSR for routing, and the ant colony algorithm for power refinement.
• The algorithm optimizes cluster structure and power configuration virtually at network initialization.

Main Results:

• The proposed method achieved a 90.4% reduction in transmission power compared to the standalone SPMA protocol.
• Success rates improved by 63.5% compared to SPMA and 162.3% compared to the ICW algorithm under high traffic loads.
• The method demonstrated a well-balanced compromise between power consumption and transmission reliability.

Conclusions:

• The joint clustering and power optimization method is feasible and effective for UAV swarms in frequency-constrained environments.
• This approach significantly enhances communication reliability and efficiency compared to existing methods.
• The optimized strategy offers a practical solution for improving UAV swarm operations.