Title: Physics-Constrained Inc-GAN for Tunnel Propagation Modeling from Sparse Line Measurements Show Chinese title

Title: 基于稀疏线测量的物理约束Inc-GAN隧道传播建模

Abstract: High-speed railway tunnel communication systems require reliable radio wave propagation prediction to ensure operational safety. However, conventional simulation methods face challenges of high computational complexity and inability to effectively process sparse measurement data collected during actual railway operations. This letter proposes an inception-enhanced generative adversarial network (Inc-GAN) that can reconstruct complete electric field distributions across tunnel cross-sections using sparse value lines measured during actual train operations as input. This directly addresses practical railway measurement constraints. Through an inception-based generator architecture and progressive training strategy, the method achieves robust reconstruction from single measurement signal lines to complete field distributions. Numerical simulation validation demonstrates that Inc-GAN can accurately predict electric fields based on measured data collected during actual train operations, with significantly improved computational efficiency compared to traditional methods, providing a novel solution for railway communication system optimization based on real operational data. Show Chinese abstract

Abstract: 高速铁路隧道通信系统需要可靠的无线电波传播预测以确保运行安全。 然而，传统仿真方法面临计算复杂度高和无法有效处理实际铁路运营中收集的稀疏测量数据的挑战。 本文提出了一种增强型生成对抗网络（Inc-GAN），可以使用实际列车运行期间测量的稀疏值线作为输入，重建隧道截面上完整的电场分布。 这直接解决了实际铁路测量的限制。 通过基于inception的生成器架构和渐进式训练策略，该方法实现了从单个测量信号线到完整场分布的鲁棒重建。 数值仿真验证表明，Inc-GAN可以根据实际列车运行期间收集的测量数据准确预测电场，与传统方法相比计算效率显著提高，为基于实际运行数据的铁路通信系统优化提供了一种新解决方案。