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arxiv: 2507.16600 · v3 · submitted 2025-07-22 · 💻 cs.IT · math.IT

A Robust 5G Terrestrial Positioning System with Sensor Fusion in GNSS-denied Scenarios

Hamed Talebian , Nazrul Mohamed Nazeer , Darius Chmieliauskas , Jakub Nikonowicz , Mehdi Haghshenas , {\L}ukasz Matuszewski , Mairo Leier , Aamir Mahmood This is my paper

Pith reviewed 2026-05-19 03:19 UTC · model grok-4.3

classification 💻 cs.IT math.IT
keywords 5G positioningGNSS-denied localizationcarrier phase rangingsensor fusionNLOS detectiondeep learningtrilaterationurban navigation
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The pith

A 5G system using carrier phase ranging, NLOS detection, and sensor fusion achieves under 5 meters positioning error in urban GNSS-denied areas.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper sets out to establish that 5G cellular networks can serve as a reliable standalone positioning service when satellite signals are blocked by buildings or other obstacles. It combines multi-carrier carrier phase measurements for direct distance estimates, a deep learning classifier to discard obstructed links, and an error-state Kalman filter that blends inertial and camera data to maintain continuity during outages. A sympathetic reader would care because the approach is tested on real urban driving trajectories and reaches accuracy levels comparable to everyday commercial GNSS services. The work therefore points toward terrestrial networks that are planned from the start to support both communication and precise location.

Core claim

The central claim is that a terrestrial positioning architecture built on 5G infrastructure can localize a moving user to less than 5 meters error in urban settings by performing trilateration with multi-carrier carrier phase ranges from at least three line-of-sight base stations, using a neural network to identify and exclude non-line-of-sight links, and applying an error-state extended Kalman filter to fuse inertial measurement unit and camera observations whenever direct ranging becomes unreliable, as demonstrated through simulation on the KITTI dataset.

What carries the argument

Multi-carrier carrier phase trilateration combined with deep-learning NLOS link classification and error-state extended Kalman filter fusion of IMU and camera data.

If this is right

  • With three or more line-of-sight base stations, the multi-carrier phase method supplies range measurements without separate integer ambiguity resolution.
  • The trained deep learning model removes non-line-of-sight measurements before trilateration, preserving accuracy under partial obstruction.
  • When line-of-sight is lost, the error-state Kalman filter keeps continuous tracking by incorporating inertial and visual updates.
  • Overall system error stays below 5 meters on urban driving data, matching typical commercial GNSS performance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same sensor-fusion pipeline could be tested on pedestrian or indoor trajectories if 5G small-cell density increases.
  • Network operators might treat positioning accuracy as a primary design goal rather than an add-on service.
  • Real-time implementation on mobile devices would need to verify that the deep learning classifier runs within the latency budget of live 5G links.

Load-bearing premise

The simulation that overlays synthetic 5G carrier phase signals onto the KITTI vehicle trajectories correctly reproduces the real propagation conditions, base-station placement, and channel effects that would occur in an actual 5G network deployment.

What would settle it

A live measurement campaign that records positioning error while driving the same routes with actual 5G base stations transmitting at the modeled frequencies and powers.

Figures

Figures reproduced from arXiv: 2507.16600 by Aamir Mahmood, Darius Chmieliauskas, Hamed Talebian, Jakub Nikonowicz, {\L}ukasz Matuszewski, Mairo Leier, Mehdi Haghshenas, Nazrul Mohamed Nazeer.

Figure 1
Figure 1. Figure 1: System-level depiction of the designed positioning and tracking [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Compares two network planning strategies: (1) The four blue cake [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: Simulated RF coverage in a suburban environment using four BSs. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Example SRS configuration within the NR OFDMA grid. SRS [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Phase spectrum at the transmitter and receiver, showing the phase [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Virtual wave from phase difference ∆Φ of two subcarriers SC1 and SC2. By substituting frequency for wavelength in (1), it can be demonstrated that the virtual wavelength is directly influenced by the frequency difference ∆f of the used subcarriers: λv = 1 f2 c − f1 c = c ∆f . (3) In this context, ∆f corresponds to the subcarrier spacing (SCS). Consequently, the differential analysis of any subcarrier pair … view at source ↗
Figure 7
Figure 7. Figure 7: Phase spectrum at the receiver after phase offset correction and [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Phase spectrum at the receiver after phase offset correction and [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The Euclidean distances of the receiver to each of the transmitters. [PITH_FULL_IMAGE:figures/full_fig_p009_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: DL classifier performance in terms of accuracy and auc, smoothed [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: DL prediction accuracy, normalized by rows [PITH_FULL_IMAGE:figures/full_fig_p010_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Cumulative distribution function of 2D/3D positioning errors under [PITH_FULL_IMAGE:figures/full_fig_p011_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Flow chart of the proposed VO and EKF system. [PITH_FULL_IMAGE:figures/full_fig_p012_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Sequence 09 given by the black trajectory overlayed on the [PITH_FULL_IMAGE:figures/full_fig_p013_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Visual odometry, SRS, and PRS-based trajectory estimates on [PITH_FULL_IMAGE:figures/full_fig_p013_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Estimated trajectories using ES-EKF for both SRS and PRS based [PITH_FULL_IMAGE:figures/full_fig_p013_16.png] view at source ↗
read the original abstract

This paper presents a terrestrial localization system based on 5G infrastructure as a viable alternative to GNSS, particularly in scenarios where GNSS signals are obstructed or unavailable. It discusses network planning aimed at enabling positioning as a primary service, in contrast to the traditional focus on communication services in terrestrial networks. Building on a network infrastructure optimized for positioning, the paper proposes a system that leverages carrier phase (CP) ranging in combination with trilateration to localize the user within the network when at least three base stations (BSs) provide line-of-sight (LOS) conditions. Achieving accurate CP-based positioning requires addressing three key challenges: integer ambiguity resolution, LOS/NLOS link identification, and localization under obstructed LOS conditions. To this end, the system employs a multi-carrier CP approach, which eliminates the need for explicit integer ambiguity estimation. Additionally, a deep learning model is developed to identify NLOS links and exclude them from the trilateration process. In cases where LOS is obstructed and CP ranging becomes unreliable, the system incorporates an error-state extended Kalman filter to fuse complementary data from other sensors, such as inertial measurement units (IMUs) and cameras. This hybrid approach enables robust tracking of moving users across diverse channel conditions. The performance of the proposed terrestrial positioning system is evaluated using the real-world KITTI dataset, featuring a moving vehicle in an urban environment. Simulation results show that the system can achieve a positioning error of less than 5 meters in the KITTI urban scenario--comparable to that of public commercial GNSS services--highlighting its potential as a resilient and accurate solution for GNSS-denied environments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a 5G terrestrial positioning system for GNSS-denied urban scenarios. It combines multi-carrier carrier-phase ranging and trilateration (when at least three LOS base stations are available), a deep-learning classifier to detect and exclude NLOS links, and an error-state EKF that fuses IMU and camera measurements when LOS is obstructed. Performance is assessed via simulations that overlay 5G carrier-phase signals on trajectories from the KITTI urban driving dataset, with the central claim that the system achieves positioning error below 5 m—comparable to commercial GNSS.

Significance. If the simulation parameters and channel models prove realistic, the hybrid CP-plus-sensor-fusion approach could offer a practical alternative for resilient positioning in GNSS-denied environments, particularly for vehicular applications. The work correctly identifies the three core challenges (integer ambiguity, NLOS identification, and partial LOS) and proposes concrete mitigations. The absence of real 5G hardware measurements or 3GPP-validated channel statistics, however, substantially reduces the strength of the performance claim.

major comments (2)
  1. [Simulation results / Evaluation] Simulation results section: the claim of <5 m error rests on an unvalidated overlay of 5G carrier-phase signals onto KITTI trajectories. No justification is given for base-station density, carrier frequency, phase-noise levels, LOS/NLOS statistics, or multipath severity; these quantities are not shown to match 3GPP urban macro or micro models or any real 5G deployment. Because the headline accuracy result is produced directly from these assumptions, the simulation setup is load-bearing for the central claim.
  2. [Proposed method / NLOS identification] Deep-learning NLOS classifier subsection: the manuscript describes the use of a DL model to identify NLOS links but supplies neither the network architecture, input features, training corpus, nor quantitative metrics (precision, recall, or confusion matrix). Without these details it is impossible to assess whether the classifier meaningfully improves trilateration accuracy or merely removes links under optimistic conditions.
minor comments (2)
  1. [Abstract] Abstract: the performance claim is stated without any numerical error statistics, simulation parameters, or DL accuracy figures; a single-sentence summary of these quantities would improve readability.
  2. [Throughout] Notation consistency: ensure that CP, LOS, NLOS, and EKF are defined at first use and used uniformly; several passages switch between “carrier phase” and “CP” without clear antecedent.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thorough review and valuable suggestions. We address the major comments point-by-point below, agreeing where revisions are needed to improve clarity and rigor.

read point-by-point responses

  1. Referee: [Simulation results / Evaluation] Simulation results section: the claim of <5 m error rests on an unvalidated overlay of 5G carrier-phase signals onto KITTI trajectories. No justification is given for base-station density, carrier frequency, phase-noise levels, LOS/NLOS statistics, or multipath severity; these quantities are not shown to match 3GPP urban macro or micro models or any real 5G deployment. Because the headline accuracy result is produced directly from these assumptions, the simulation setup is load-bearing for the central claim.

    Authors: We recognize that the simulation setup requires more detailed justification to align with realistic conditions. In the revised manuscript, we will expand the simulation results section to provide explicit justifications for base-station density, carrier frequency, phase-noise levels, LOS/NLOS statistics, and multipath severity. These will be chosen and validated against 3GPP TR 38.901 urban macro and micro channel models, with references to relevant literature on 5G deployments. We will also include sensitivity analyses to assess the impact of variations in these parameters on the positioning error, thereby supporting the robustness of the <5 m accuracy claim. revision: yes

  2. Referee: [Proposed method / NLOS identification] Deep-learning NLOS classifier subsection: the manuscript describes the use of a DL model to identify NLOS links but supplies neither the network architecture, input features, training corpus, nor quantitative metrics (precision, recall, or confusion matrix). Without these details it is impossible to assess whether the classifier meaningfully improves trilateration accuracy or merely removes links under optimistic conditions.

    Authors: We agree that the description of the deep learning-based NLOS classifier lacks necessary implementation details. We will revise the manuscript to provide a complete description, including the neural network architecture (specifying layers, activation functions, and input dimensions), the extracted features from the received signals, details on the training dataset (including how it was generated or sourced), and evaluation metrics such as accuracy, precision, recall, and the confusion matrix. Furthermore, we will include results demonstrating the classifier's contribution to the overall system performance through comparative experiments with and without the NLOS identification module. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation or evaluation chain

full rationale

The paper proposes a 5G positioning architecture using multi-carrier carrier-phase ranging (to sidestep integer ambiguity), a deep-learning NLOS classifier, trilateration, and EKF-based fusion with IMU/camera data. The headline performance claim (<5 m error on KITTI urban trajectories) is obtained by applying this pipeline to synthetically overlaid 5G signals on real vehicle trajectories. No equation or result is shown to reduce, by construction, to a fitted parameter that is then re-labeled as a prediction, nor does any load-bearing step rest on a self-citation whose content is itself unverified. The simulation parameters are presented as design choices rather than quantities derived from the target accuracy metric. Consequently the reported positioning error is an independent empirical outcome of the described components rather than a tautological restatement of the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the validity of the multi-carrier approach eliminating integer ambiguity, the deep learning model's ability to classify LOS/NLOS correctly, and the realism of the KITTI-based simulation. No explicit free parameters are detailed in the abstract.

axioms (1)
  • domain assumption At least three base stations with line-of-sight conditions are available for trilateration.
    The abstract states this as a requirement for the CP-based positioning to work.

pith-pipeline@v0.9.0 · 5861 in / 1531 out tokens · 76886 ms · 2026-05-19T03:19:04.441998+00:00 · methodology

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Reference graph

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