AGI-inspired UAV Cluster Communication Self-Organization Framework: Multi-Agent Learning and Network Optimization

Abstract

Unmanned Aerial Vehicle (UAV) cluster communication networks play a vital role in Space-Air-Ground Integrated Networks (SAGIN), offering flexible deployment and high mobility for emergency communications, disaster relief, and edge computing. However, traditional approaches face significant limitations in dynamic environments, often focusing on single objectives, lacking adaptive capabilities, and failing to achieve global coordination across multiple UAVs. To address these challenges, this paper presents Artificial General Intelligence-inspired Multi-Agent Knowledge Transfer (AGI-MAKT), a novel self-organizing framework for UAV cluster communication networks. Our framework overcomes existing limitations through several key innovations: a multi-modal perception system that enables comprehensive environmental understanding by fusing heterogeneous data sources; a hierarchical multi-agent learning architecture that reduces decision-making complexity while enhancing coordination; an adaptive multi-objective optimization approach that dynamically balances coverage, energy efficiency, and throughput based on contextual requirements; and efficient knowledge transfer mechanisms that accelerate learning in new environments. Comprehensive experiments demonstrate that AGI-MAKT consistently outperforms representative baselines across the evaluated performance metrics while maintaining strong robustness under challenging conditions. This research establishes a foundation for next-generation aerial networks capable of self-organization and adaptation in complex, resource-constrained environments with limited human supervision.