Literature Database Entry

basaran2025gen-twin

Osman Tugay Basaran, Davide Villa, Pedram Johari, Michele Polese, Claudio Fiandrino, Falko Dressler and Tommaso Melodia, "Gen-TWIN: Generative-AI-Enabled Digital Twin for Open Radio Access Networks," Proceedings of 44th IEEE International Conference on Computer Communications (INFOCOM 2025), Digital Twins over NextG Wireless Networks (DTWin 2025), London, United Kingdom, May 2025. (to appear)

Abstract

The realization of efficient Artificial Intelligence (AI) solutions for the optimization of next-generation Radio Access Network (RAN) relies on the availability of expansive, high-quality datasets that accurately capture nuanced, site-specific conditions. However, obtaining such abundant, domain-specific measurements poses a significant challenge, especially as network complexity and energy efficiency demand surge toward 6G. In response, we introduce GenerativeAI-enabled Digital Twin (Gen-TWIN), a synthetic data generation framework underpinned by a soft-attention LSTM-based generative adversarial network (soft-GAN). Our model augments realistic transmitter and receiver-focused RF datasets by supplementing scarce empirical samples and providing the synthetic data volumes essential for training advanced AI models on RAN. Accuracy results show that soft-GAN provided 19% performance improvement compared to baseline models.

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Osman Tugay Basaran
Davide Villa
Pedram Johari
Michele Polese
Claudio Fiandrino
Falko Dressler
Tommaso Melodia

BibTeX reference

@inproceedings{basaran2025gen-twin,
    author = {Basaran, Osman Tugay and Villa, Davide and Johari, Pedram and Polese, Michele and Fiandrino, Claudio and Dressler, Falko and Melodia, Tommaso},
    note = {to appear},
    title = {{Gen-TWIN: Generative-AI-Enabled Digital Twin for Open Radio Access Networks}},
    publisher = {IEEE},
    address = {London, United Kingdom},
    booktitle = {44th IEEE International Conference on Computer Communications (INFOCOM 2025), Digital Twins over NextG Wireless Networks (DTWin 2025)},
    month = {5},
    year = {2025},
   }
   
   

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