Gen-TWIN: Generative-AI-Enabled Digital Twin for Open Radio Access Networks

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.