A Generative AI-Enhanced Digital Twin Framework for Proactive Interference Management in Hybrid Near/Far-Field Wireless Systems
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909031300333568 |
|---|---|
| author | Ali, Afan Nasir, Ali Arshad da Costa, Daniel Benevides |
| author_facet | Ali, Afan Nasir, Ali Arshad da Costa, Daniel Benevides |
| contents | The applications of Digital Twins (DT) and Generative AI (GenAI) have demonstrated their capabilities in modeling and learning-based wireless communications. However, their joint potential for proactive wireless system design remains largely underexplored, particularly in extremely large-scale multiple-input multiple-output (XL-MIMO) networks, characterized by hybrid near-field (NF) and far-field (FF) propagation regimes. In this work, we propose an integrated GenAI-enhanced DT framework for proactive interference management in dynamic indoor scenarios. The DT constructs a high-resolution, site-specific virtual replica of the deployment environment, understanding where and why blockage occurs within a realistic 3D representation of the indoor space. Integration of the GenAI module further assists the framework in anticipating and proactively suppressing blockage, rather than reacting after the disruption occurs. Extensive simulation results based on Sionna ray-tracing datasets demonstrate that the proposed framework achieves significant improvements in interference suppression, signal-to-interference-plus-noise ratio (SINR), and outage probability compared to conventional reactive schemes and purely deterministic DT-based approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10145 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Generative AI-Enhanced Digital Twin Framework for Proactive Interference Management in Hybrid Near/Far-Field Wireless Systems Ali, Afan Nasir, Ali Arshad da Costa, Daniel Benevides Signal Processing The applications of Digital Twins (DT) and Generative AI (GenAI) have demonstrated their capabilities in modeling and learning-based wireless communications. However, their joint potential for proactive wireless system design remains largely underexplored, particularly in extremely large-scale multiple-input multiple-output (XL-MIMO) networks, characterized by hybrid near-field (NF) and far-field (FF) propagation regimes. In this work, we propose an integrated GenAI-enhanced DT framework for proactive interference management in dynamic indoor scenarios. The DT constructs a high-resolution, site-specific virtual replica of the deployment environment, understanding where and why blockage occurs within a realistic 3D representation of the indoor space. Integration of the GenAI module further assists the framework in anticipating and proactively suppressing blockage, rather than reacting after the disruption occurs. Extensive simulation results based on Sionna ray-tracing datasets demonstrate that the proposed framework achieves significant improvements in interference suppression, signal-to-interference-plus-noise ratio (SINR), and outage probability compared to conventional reactive schemes and purely deterministic DT-based approaches. |
| title | A Generative AI-Enhanced Digital Twin Framework for Proactive Interference Management in Hybrid Near/Far-Field Wireless Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.10145 |