{"ID":6267047,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-13T01:02:08.706470581Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.08141","arxiv_id":"2607.08141","title":"GenAI-Enhanced Digital Twins for Predictive Interference Management in Ultra-Dense Networks","abstract":"Ultra-dense indoor next-generation networks suffer severe interference from mobility-induced blockages and localized multi-user hotspots that conventional digital twins~(DTs) cannot anticipate. We propose a generative AI~(GenAI)-enhanced DT framework employing a conditional generative adversarial network~(cGAN) with a spatio-temporal generator and PatchGAN discriminator for proactive rare-event channel synthesis. A worst-case zero-forcing~(WC-ZF) beamformer driven by Monte Carlo synthetic trajectories realizes distributionally robust precoding, with control-channel overhead bounded to $\\approx$2.1\\,kB per 10\\,ms slot. Sionna-based simulations confirm a 5--8\\,dB median signal-to-interference-plus-noise-ratio (SINR) gain, 60--70\\% packet-loss reduction, and 60--85\\% closure of the perfect channel state information (CSI) oracle gap within a 2.8--4.1\\,ms inference overhead.","short_abstract":"Ultra-dense indoor next-generation networks suffer severe interference from mobility-induced blockages and localized multi-user hotspots that conventional digital twins~(DTs) cannot anticipate. We propose a generative AI~(GenAI)-enhanced DT framework employing a conditional generative adversarial network~(cGAN) with a...","url_abs":"https://arxiv.org/abs/2607.08141","url_pdf":"https://arxiv.org/pdf/2607.08141v1","authors":"[\"Afan Ali\",\"Ali Arshad Nasir\",\"Daniel Benevides da Costa\"]","published":"2026-07-09T06:21:41Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}