Opportunistic Navigation Exploiting Always-On and On-Demand 5G Downlink Signals on a Ground Vehicle

An opportunistic navigation receiver that exploits always-on and on-demand 5G downlink signals is presented. The semi-cognitive receiver operates in two stages: (i) acquisition, which utilizes the "always-on" signals to detect gNBs, and (ii) Kalman filter (KF)-based tracking, which continuously estimates the on-demand reference signals (RSs) to refine the receiver’s local replica. Experimental results show that the estimated replica effectively utilizes nearly the entire channel bandwidth, spans almost all orthogonal frequency division multiplexing (OFDM) symbols for longer integration time, and achieves higher processing gain, enhancing the carrier-to-noise ratio for reliable acquisition and tracking. An experiment with real 5G signals on a ground vehicle demonstrated a significant 62% reduction in position root-mean squared error (RMSE) compared to a conventional opportunistic navigation 5G receiver which only utilized always-on signals.

Authors

Institutions

Publication Details

Published
2024-10-07
DOI
https://doi.org/10.1109/vtc2024-fall63153.2024.10757621
Citations
1
Primary Topic
Vehicular Ad Hoc Networks (VANETs)
Type
article
Field-Weighted Citation Impact
0.21

Funders

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

Opportunistic Navigation Exploiting Always-On and On-Demand 5G Downlink Signals on a Ground Vehicle

Zaher M. Kassas, Sharbel Kozhaya, Samer Hayek, Faezeh Mooseli
1 citations
Vehicular Ad Hoc Networks (VANETs)
0.21
article

Opportunistic Navigation Exploiting Always-On and On-Demand 5G Downlink Signals on a Ground Vehicle

Zaher M. Kassas, Sharbel Kozhaya, Samer Hayek, Faezeh Mooseli
article en
1 citations

Abstract

An opportunistic navigation receiver that exploits always-on and on-demand 5G downlink signals is presented. The semi-cognitive receiver operates in two stages: (i) acquisition, which utilizes the "always-on" signals to detect gNBs, and (ii) Kalman filter (KF)-based tracking, which continuously estimates the on-demand reference signals (RSs) to refine the receiver’s local replica. Experimental results show that the estimated replica effectively utilizes nearly the entire channel bandwidth, spans almost all orthogonal frequency division multiplexing (OFDM) symbols for longer integration time, and achieves higher processing gain, enhancing the carrier-to-noise ratio for reliable acquisition and tracking. An experiment with real 5G signals on a ground vehicle demonstrated a significant 62% reduction in position root-mean squared error (RMSE) compared to a conventional opportunistic navigation 5G receiver which only utilized always-on signals.

The Ohio State University (US)
U.S. Department of Transportation, Air Force Office of Scientific Research
Openalex Percentile: Top 44%
Vehicular Ad Hoc Networks (VANETs)
0.21
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.

Opportunistic Navigation Exploiting Always-On and On-Demand 5G Downlink Signals on a Ground Vehicle — Zaher M. Kassas, Sharbel Kozhaya, et al. · (2024) | TGRS Research Map | TGRS