Small Models, Big Impact: Tool-Augmented AI Agents for Wireless Network Planning

TL;DR

This paper presents MAINTAINED, a tool-augmented AI system for wireless network planning that outperforms large language models in accuracy and efficiency by externalizing domain knowledge into specialized computational tools.

Contribution

Introduces MAINTAINED, an autonomous AI agent that uses external computational tools for wireless network deployment, reducing reliance on large language models and eliminating hallucinations.

Findings

MAINTAINED outperforms state-of-the-art LLMs by up to 100-fold in verified metrics.

Requires less computational resources than traditional LLM-based approaches.

Enables edge-deployable AI for wireless communications.

Abstract

Large Language Models (LLMs) such as ChatGPT promise revolutionary capabilities for Sixth-Generation (6G) wireless networks but their massive computational requirements and tendency to generate technically incorrect information create deployment barriers. In this work, we introduce MAINTAINED: autonomous artificial intelligence agent for wireless network deployment. Instead of encoding domain knowledge within model parameters, our approach orchestrates specialized computational tools for geographic analysis, signal propagation modeling, and network optimization. In a real-world case study, MAINTAINED outperforms state-of-the-art LLMs including ChatGPT-4o, Claude Sonnet 4, and DeepSeek-R1 by up to 100-fold in verified performance metrics while requiring less computational resources. This paradigm shift, moving from relying on parametric knowledge towards externalizing domain knowledge into verifiable computational tools, eliminates hallucination in technical specifications and enables edge-deployable Artificial Intelligence (AI) for wireless communications.