An AI-Powered Multi-Agent Ecosystem for Cost-Effective Planning and Expansion of Telecommunication Access Network
The expansion of telecommunication access networks is constrained by static planning methods unable to process diverse, dynamic data. To address this, we propose a novel Multi-Agent System (MAS) where autonomous, domain-specialized AI agents collaboratively evaluate criteria for network expansion. The framework uniquely integrates structured and geospatial data with insights from unstructured documents via a Retrieval-Augmented Generation (RAG) component and synthesizes the agents' collective findings using the Analytic Hierarchy Process (AHP) to transparently weigh decision factors. This work provides a scalable, explainable, and methodologically robust framework for dynamic network planning.