Enabling Large Language Model Based Data Synthesis in Wireless Mesh Network Configuration for Internet of Things

Abstract

Wireless Mesh Networks (WMNs) are essential for many Internet of Things (IoT) applications, such as industrial automation, environmental monitoring, and smart cities. Today, configuring a WMN to meet its stringent performance requirements remains a significant challenge due to dynamic real-world wireless conditions and operating environments. The simulationto-reality gap in network configuration further complicates the generalization of models trained solely with simulation data, leading to suboptimal performance in physical deployments. To address such challenges, we develop WMN-LLM-DS, a novel framework that integrates Large Language Models (LLMs) for synthetic data generation with domain adaptation techniques to better configure WMNs. Leveraging LLMs, WMN-LLM-DS generates high-quality, diverse synthetic datasets conditioned on realworld constraints, effectively bridging the simulation-to-reality gap and enriching the diversity of training data. WMN-LLM-DS employs a teacher-student architecture to transfer network configuration knowledge learned from simulations to physical deployments, enhanced by custom loss functions to align feature representations across different domains. Experiments conducted on datasets collected from a physical testbed and network simulators demonstrate that WMN-LLM-DS outperforms existing solutions, achieving an improvement of up to 10.8% in prediction accuracy while also exhibiting strong domain generalization capabilities.