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A Machine Learning-Based Channel Data Enhancement Platform for Digital Twin Channels
AI Bo, ZHANG Yuxin, YANG Mi, HE Ruisi, GUO Rongge
ZTE Communications    2025, 23 (2): 20-30.   DOI: 10.12142/ZTECOM.202502004
Abstract60)   HTML5)    PDF (2951KB)(46)       Save

Reliable channel data helps characterize the limitations and performance boundaries of communication technologies accurately. However, channel measurement is highly costly and time-consuming, and taking actual measurement as the only channel data source may reduce efficiency because of the constraints of high testing difficulty and limited data volume. Although existing standard channel models can generate channel data, their authenticity and diversity cannot be guaranteed. To address this, we use deep learning methods to learn the attributes of limited measured data and propose a generative model based on generative adversarial networks to rapidly synthesize data. A software simulation platform is also established to verify that the proposed model can generate data that are statistically similar to the measured data while maintaining necessary randomness. The proposed algorithm and platform can be applied to channel data enhancement and serve channel modeling and algorithm evaluation applications with urgent needs for data.

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