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

TL;DR

This paper presents a novel approach to create a highly accurate digital twin of the physical environment for cellular channel state information, enabling improved site-specific precoding through AI-enhanced fine-tuning.

Contribution

It introduces a multi-step channel twin design process combining ray tracing and AI-based fine-tuning to closely replicate real environment CSI for better precoding.

Findings

The proposed method achieves a significant reduction in CSI gap.

AI-based fine-tuning improves the accuracy of the digital twin.

Simulation results validate the effectiveness of the environment-aware channel twin.

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.