Tootfinder

NGGAN: Noise Generation GAN Based on the Practical Measurement Dataset for Narrowband Powerline Communications
Ying-Ren Chien, Po-Heng Chou, You-Jie Peng, Chun-Yuan Huang, Hen-Wai Tsao, Yu Tsao
https://arxiv.org/abs/2510.01850

NGGAN: Noise Generation GAN Based on the Practical Measurement Dataset for Narrowband Powerline Communications
Capturing comprehensive statistics of nonperiodic asynchronous impulsive noise is a critical issue in enhancing impulse noise processing for narrowband powerline communication (NB-PLC) transceivers. However, existing mathematical noise generative models capture only some of the characteristics of additive noise. Therefore, we propose a generative adversarial network (GAN), called the noise-generation GAN (NGGAN), that learns the complicated characteristics of practically measured noise samples …

Physics-Constrained Inc-GAN for Tunnel Propagation Modeling from Sparse Line Measurements
Yang Zhou, Haochang Wu, Yunxi Mu, Hao Qin, Xinyue Zhang, Xingqi Zhang
https://arxiv.org/abs/2510.03019

Physics-Constrained Inc-GAN for Tunnel Propagation Modeling from Sparse Line Measurements
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 usi…