RIS-Enhanced Backscatter Communication for Vehicular Self-Localization

In this letter, we propose a novel vehicular self-localization technique leveraging reconfigurable intelligent surface (RIS)-enhanced backscatter communication. Specifically, while transmitting frequency modulated continuous wave (FMCW) signals for collision avoidance, the vehicle simultaneously estimates its distance from multiple roadside units (RSUs) and demodulates RSUs’ location information backscattered from the RISs. To enable accurate parameter estimation and data demodulation, we propose an element-wise complex division algorithm that effectively decouples the sensing parameters and data symbols. We also demonstrate that the estimated sensing information can assist in symbol demodulation, thereby significantly reducing pilot overhead. Simulation results validate that the proposed algorithm significantly improves the localization accuracy and achieves comparable demodulation error performance to that of perfect channel state information with only 1% demodulation reference signal.

Authors

Institutions

Publication Details

Journal
IEEE Wireless Communications Letters
Published
2025-03-22
DOI
https://doi.org/10.1109/lwc.2025.3553451
Citations
2
Primary Topic
Energy Harvesting in Wireless Networks
Type
article
Field-Weighted Citation Impact
1.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

RIS-Enhanced Backscatter Communication for Vehicular Self-Localization

Zesong Fei, Xiaomei Wu, Qingqing Wu, Fanghao Xia et al.
2 citations
IEEE Wireless Communications Letters
Energy Harvesting in Wireless Networks
1.46
article

RIS-Enhanced Backscatter Communication for Vehicular Self-Localization

Zesong Fei, Xiaomei Wu, Qingqing Wu, Fanghao Xia, Xinyi Wang, X. Cai, Nan Ha
article en
2 citations

Abstract

In this letter, we propose a novel vehicular self-localization technique leveraging reconfigurable intelligent surface (RIS)-enhanced backscatter communication. Specifically, while transmitting frequency modulated continuous wave (FMCW) signals for collision avoidance, the vehicle simultaneously estimates its distance from multiple roadside units (RSUs) and demodulates RSUs’ location information backscattered from the RISs. To enable accurate parameter estimation and data demodulation, we propose an element-wise complex division algorithm that effectively decouples the sensing parameters and data symbols. We also demonstrate that the estimated sensing information can assist in symbol demodulation, thereby significantly reducing pilot overhead. Simulation results validate that the proposed algorithm significantly improves the localization accuracy and achieves comparable demodulation error performance to that of perfect channel state information with only 1% demodulation reference signal.

IEEE Wireless Communications LettersVol. 14(6)
Beijing Institute of Technology (CN), Shanghai Jiao Tong University (CN), China Aerospace Science and Technology Corporation (CN)
Openalex Percentile: Top 19%
Energy Harvesting in Wireless Networks
1.46
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.