A DCOP-Based Dynamic Coordination Framework for Massive Decentralized Small-Cell Networks

Modern distributed systems face escalating coordination challenges as deployment density scales to thousands of interconnected nodes, from dense wireless networks to large-scale Internet of Things infrastructures. Distributed Constraint Optimization Problems (DCOPs) provide a powerful multi-agent framework that enables distributed decision-making while optimizing global objectives through local interactions, making them particularly well-suited for managing density-driven coordination challenges across various distributed systems. This paper presents a novel DCOP-based group-hierarchical distributed optimization framework addressing these scalability challenges, demonstrated through base station coordination in ultra-dense small cell networks. Our approach separates static pseudotree structures based on agent topology from dynamic constraint values from user equipment measurement reports, eliminating costly tree reconstruction while adapting to changing conditions.We propose a limited solving window algorithm that provides time-bounded optimization and develop a group-based hierarchical approach that constrains coordination dynamics within specific groups and layers, significantly reducing computational overhead while preserving global optimization properties. Extensive experiments in realistic urban environments demonstrate that the proposed approach can significantly improve signal quality by up to 44% compared to traditional uncoordinated networks and 11% compared to centralized optimization with fewer base station parameter adjustments and communication overhead, validating its effectiveness in managing density-driven coordination complexity.