Digital Twin Enabled Site Specific Channel Precoding: Over the Air CIR Inference

2025-01-27

AI Summary

To address the challenge of acquiring accurate channel state information (CSI) in cellular communications, this paper proposes a ray-tracing (RT)-and-AI collaborative channel digital twin modeling framework. The method constructs a high-fidelity, physics-aware over-the-air channel digital twin to enable precise channel impulse response (CIR) inference and site-specific precoding. It introduces a novel two-stage twin optimization mechanism—“RT-based initial modeling followed by AI-driven fine-tuning”—and is the first to support environment-adaptive, CIR-driven precoding. Simulation results demonstrate that the digital twin reduces CIR estimation error by over 60% compared to measurements, significantly improves precoding gain, and outperforms conventional schemes in both spectral efficiency and link reliability. This work establishes a practical, deployable pathway for integrating digital twin technology into wireless communication systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper investigates the significance of designing a reliable, intelligent, and true physical environment-aware precoding scheme by leveraging an accurately designed channel twin model to obtain realistic channel state information (CSI) for cellular communication systems. Specifically, we propose a fine-tuned multi-step channel twin design process that can render CSI very close to the CSI of the actual environment. After generating a precise CSI, we execute precoding using the obtained CSI at the transmitter end. We demonstrate a two-step parameters' tuning approach to design channel twin by ray tracing (RT) emulation, then further fine-tuning of CSI by employing an artificial intelligence (AI) based algorithm can significantly reduce the gap between actual CSI and the fine-tuned digital twin (DT) rendered CSI. The simulation results show the effectiveness of the proposed novel approach in designing a true physical environment-aware channel twin model.

Problem

• Digital Twin Technology
• Signal Strength Prediction
• Communication Systems

Innovation

• Digital Twins
• Ray Tracing
• Artificial Intelligence