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

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.

Authors

Institutions

Publication Details

Journal
IEEE Communications Letters
Published
2025-05-07
DOI
https://doi.org/10.1109/lcomm.2025.3567850
Citations
4
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
4.97

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Lingjia Liu, Danda B. Rawat, Imtiaz Ahmed, Md. Zoheb Hassan et al.
4 citations
IEEE Communications Letters
Industrial Vision Systems and Defect Detection
4.97
article

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

Lingjia Liu, Danda B. Rawat, Imtiaz Ahmed, Md. Zoheb Hassan, Majumder Haider, Timothy J. O’Shea
article en
4 citations

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.

IEEE Communications LettersVol. 29(7)
Howard University (US), Université Laval (CA), Virginia Tech (US)
National Science Foundation
Openalex Percentile: Top 5%
Industrial Vision Systems and Defect Detection
4.97
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Digital Twin Enabled Site Specific Channel Precoding: Over the Air CIR Inference — Lingjia Liu, Danda B. Rawat, et al. · IEEE Communications Letters (2025) | TGRS Research Map | TGRS