: Domain adaptation remains a significant challenge in deploying data-driven models under distribution shifts, particularly when transferring from simulated to real-world environments. Existing approaches often rely on large labeled target datasets, suffer negative transfer, and provide limited interpretability. In this paper, we present NoiseFlow, a data-efficient domain adaptation framework that leverages noise-aware modeling and flow matching to enable robust cross-domain generalization. Our key insight is that feature dimensions exhibit heterogeneous sensitivity to noise, which can be amplified under domain shift. NoiseFlow introduces a feature-aware teacher student architecture that combines knowledge distillation, distribution alignment, and continuous flow matching to learn smooth transformations between source and target domains. Experimentation on wireless network configuration tasks demonstrates that NoiseFlow achieves good performance in low-data regimes, reaching 69.8% accuracy with a single target sample and improving zero-shot transfer performance by up to 40% over existing methods.
Aitian Ma, Dongsheng Luo, M. Sha· Proceedings of the 15th Inte...· 0 citations
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.
Aitian Ma, Jean Marco Cruz, Dongsheng Luo et al.· Annual International Compute...· 1 citation
