A Generative AI-Enhanced Digital Twin Framework for Proactive Interference Management in Hybrid Near/Far-Field Wireless Systems

TL;DR

This paper introduces a GenAI-enhanced Digital Twin framework for proactive interference management in complex indoor XL-MIMO wireless systems, improving signal quality and reducing outages.

Contribution

It presents a novel integrated framework combining Digital Twins and Generative AI for proactive interference suppression in hybrid near/far-field wireless environments.

Findings

Significant improvements in interference suppression and SINR.

Reduction in outage probability compared to traditional methods.

Effective modeling of blockage in realistic indoor environments.

Abstract

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.