Adapting Wireless Network Configuration From Simulation to Reality via Deep Learning-Based Domain Adaptation
Adapting Wireless Network Configuration From Simulation to Reality via Deep Learning-Based Domain Adaptation
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DOI：
10.1109/tnet.2023.3335346
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发表时间：
2024-06
期刊：
影响因子：
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通讯作者：
Junyang Shi;Aitian Ma;Xia Cheng;Mo Sha;Peng Xi
中科院分区：
文献类型：
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作者：
Junyang Shi;Aitian Ma;Xia Cheng;Mo Sha;Peng Xi
如今，无线网状网络（WMN）已在全球范围内部署，以支持各种应用，如工业自动化、军事行动和智能能源。在文献中已经做出了重大努力，以促进其部署和优化其性能。然而，配置一个WMN是具有挑战性的，因为网络配置是一个复杂的过程，其中涉及理论计算，仿真和现场测试，以及其他任务。我们的研究表明，从模拟中学习的网络配置预测模型在物理网络中可能无法很好地工作，因为模拟与现实的差距。在本文中，我们采用基于深度学习的领域自适应来缩小差距，并利用师生神经网络和物理采样方法将从模拟网络学习到的网络配置知识转移到相应的物理网络。实验结果表明，我们的方法有效地缩小了差距，并提高了预测一个良好的网络配置，使网络满足性能要求的准确性从30.10%到70.24%，通过学习强大的机器学习模型，从大量廉价的仿真数据和一些昂贵的现场测试测量。
Today, wireless mesh networks (WMNs) are deployed globally to support various applications, such as industrial automation, military operations, and smart energy. Significant efforts have been made in the literature to facilitate their deployments and optimize their performance. However, configuring a WMN well is challenging because the network configuration is a complex process, which involves theoretical computation, simulation, and field testing, among other tasks. Our study shows that the models for network configuration prediction learned from simulations may not work well in physical networks because of the simulation-to-reality gap. In this paper, we employ deep learning-based domain adaptation to close the gap and leverage a teacher-student neural network and a physical sampling method to transfer the network configuration knowledge learned from a simulated network to its corresponding physical network. Experimental results show that our method effectively closes the gap and increases the accuracy of predicting a good network configuration that allows the network to meet performance requirements from 30.10% to 70.24% by learning robust machine learning models from a large amount of inexpensive simulation data and a few costly field testing measurements.