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

Yongqiang Zhang, Mustafa A. Kishk, Mohamed-Slim Alouini

TL;DR

The paper tackles the challenge of deploying large language models for wireless network planning by addressing hallucination through tool augmentation. It introduces MAINTAINED, a framework that uses function calling and the ReAct reasoning-execution loop to orchestrate domain-specific tools for geographic data collection, physics-based propagation analysis, and constrained optimization, thereby externalizing knowledge into verifiable computations. A key result is that a compact 4B-parameter model with tool orchestration can outperform much larger models (e.g., ChatGPT-4o, Claude Sonnet 4, DeepSeek-R1) by up to 100x in verified performance while reducing resource demands and enabling edge deployment. This approach demonstrates improved accuracy, transparency, and efficiency for wireless network planning and suggests a broader shift toward hybrid systems that couple reasoning agents with reliable external computation in engineering domains.

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.

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

TL;DR

The paper tackles the challenge of deploying large language models for wireless network planning by addressing hallucination through tool augmentation. It introduces MAINTAINED, a framework that uses function calling and the ReAct reasoning-execution loop to orchestrate domain-specific tools for geographic data collection, physics-based propagation analysis, and constrained optimization, thereby externalizing knowledge into verifiable computations. A key result is that a compact 4B-parameter model with tool orchestration can outperform much larger models (e.g., ChatGPT-4o, Claude Sonnet 4, DeepSeek-R1) by up to 100x in verified performance while reducing resource demands and enabling edge deployment. This approach demonstrates improved accuracy, transparency, and efficiency for wireless network planning and suggests a broader shift toward hybrid systems that couple reasoning agents with reliable external computation in engineering domains.

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.
Paper Structure (23 sections, 4 figures, 2 tables)

This paper contains 23 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Function Calling and ReAct Agents
  3. Function Calling
  4. Zero Hallucination for Computational Results
  5. Access to Up-to-Date Information
  6. Domain-Specific Tool Integration
  7. Inference Cost Reduction
  8. ReAct Framework
  9. Dynamic Adaptation and Error Recovery
  10. Transparent Decision-Making Process
  11. Flexible Problem Decomposition
  12. Reduced Model Size Requirements
  13. The MAINTAINED Framework
  14. Task Specification Through Natural Language
  15. Intelligent Tool Coordination
  16. ...and 8 more sections

Figures (4)

  • Figure 1: Illustration of function calling in LLM systems.
  • Figure 2: The ReAct cycle.
  • Figure 3: The architecture of our proposed MAINTAINED framework.
  • Figure 4: Execution workflow and output visualization of the proposed framework.