ChatGPT-Assisted Network Optimization: Leveraging Large Language Models for Adaptive Communication Protocols

Abstract

The evolution of communication networks has witnessed an increasing demand for intelligent, adaptive, and self-optimizing protocols to handle dynamic data flow and user needs. This study investigates the integration of ChatGPT and other large language models (LLMs) into network optimization frameworks, enabling context-aware decision-making, predictive resource allocation, and real-time traffic management. By simulating multi-agent communication systems supported by LLM-based controllers, the research demonstrates improved throughput, latency reduction, and enhanced fault tolerance in adaptive protocol environments. Results indicate that LLM-assisted optimization can increase network efficiency by up to 22% compared to traditional heuristic-based models. The study further explores ethical and computational implications, highlighting the potential of AI-driven natural language models to revolutionize adaptive communication systems in 5G, 6G, and IoT networks.