Uplink SRS-Based Real-Time Indoor Localization System over OpenAirInterface

Chieh-Chun Chen; Navid Nikaein; Ping-Yu Hsieh; Ray-Guang Cheng

REVIEW 3 major objections 4 minor 10 references

Uplink SRS-Based Real-Time Indoor Localization System over OpenAirInterface

T0 review · 3 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read Uplink SRS telemetry, routed through the E2 interface, can locate a smartphone indoors with 0.12 m mean error — but only inside the surveyed area, since spatially unseen regions degrade to 1.78 m.

desk verdict A useful OAI integration that is honest about its own limitation: the 0.12 m MAE is interpolation; the real spatial-split number is 1.78 m. read the letter →

arxiv 2607.18549 v1 pith:MYPHXJQ5 submitted 2026-07-20 cs.NI

Ping-Yu Hsieh , Chieh-Chun Chen , Navid Nikaein , Ray-Guang Cheng This is my paper · ORCID

classification cs.NI

keywords IndoorlocalizationSoundingReferenceSignalChannelFrequencyResponseRandomForestO-RANxAppIntegratedSensingandCommunication5GpositioningReal-time

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that the uplink Sounding Reference Signal telemetry a 5G base station already receives can be exploited to locate a commercial smartphone indoors with sub-meter error. The authors build a positioning application that converts the channel frequency response into a 32-dimensional feature vector and feeds a Random Forest regressor. In a dense grid inside a 6-by-6 meter multipath laboratory, they measure 0.12 m mean absolute error with a random 80/20 data split, and 1.78 m when entire spatial regions are held out. The result points to an economical path for integrated sensing and communication on standard 5G radio hardware, with the caveat that the current model only interpolates within surveyed areas.

What carries the argument

The central object is the 32-dimensional physics-aware feature vector derived from the 4-antenna channel frequency response. It combines a 16-tap normalized power delay profile, 8 trigonometric inter-antenna phase difference components, and 8 received-power/symmetry heuristics. This representation lets a Random Forest regressor map radio fingerprints to coordinates without deep learning. The lower-layer control service model is the conduit that delivers the SRS telemetry; the Random Forest is the estimator.

What would settle it

Run the positioned xApp at a grid coordinate never visited during training and measure the error: if it stays near 0.12 m rather than 1.78 m, the claimed interpolation dependency is wrong. Separately, record timestamps at the base station and at the positioning app to check whether end-to-end latency actually meets the real-time claim.

Watch

Extended reading notes

Core claim

The central claim is that uplink SRS measurements, exposed through the E2 interface's lower-layer control service model, can be turned into a compact feature set — normalized power delay profile taps, inter-antenna phase differences, and received-power heuristics — whose Random Forest regression predicts 2D coordinates. The paper reports 0.1238 m MAE in the random-split evaluation and 1.78 m in spatially blind evaluation, concluding that tree-based regressors interpolate well within the surveyed region but extrapolate poorly. The pipeline runs in real time and visualizes single- and multi-UE tracking with confidence heatmaps.

Load-bearing premise

The evaluation assumes that an 80/20 random split over densely sampled grid points produces an independent test set; because frames from the same physical location can appear in both training and testing, the 0.12 m MAE is an interpolation metric, and the spatially separated split (1.78 m) shows the real generalization gap.

Editorial extensions

If this is right

If correct, any O-RAN-compliant gNB with SRS and the lower-layer control service model can host sub-meter indoor localization without modifying the UE.
The 1.78 m spatially-split result bounds the approach: the model cannot extrapolate beyond surveyed regions, so deployment requires dense site surveys.
The RSSI-only baseline's 0.2153 m MAE shows delay and phase features contribute a 42% error reduction, making multipath structure essential.
At microsecond inference per sample, the pipeline is light enough for near-real-time tracking with an EMA smoothing lag of ~1 s.
The multi-UE collision-detection effect falls out of overlapping probability heatmaps, needing no extra computation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The 0.12 m figure likely overstates accuracy in a real deployment, because the random split allows frames from the same physical grid point to appear in both training and testing; a truly unseen location is better predicted by the 1.78 m spatial-split number.
Since the authors never measure E2 interface latency or frame loss, the 'real-time' claim rests on an unverified delivery path; logging timestamps at both ends would quantify the actual end-to-end delay.
A natural extension is to swap the Random Forest for a spatially continuous regressor, such as a Gaussian process; if the spatial-split error then drops dramatically, the tree model's piecewise-constant output is the main bottleneck.
The overlapping probability heatmaps that detect multi-UE convergence could serve as a zero-cost proximity alert in future integrated sensing and communication systems, but the false-alarm rate needs quantification.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a genuine integration effort — an uplink SRS-based localization xApp on OAI/FlexRIC, using a commercial phone, with a 32-D physics-aware feature set and a random forest regressor. The 0.12 m MAE in the abstract is real but it is an interpolation metric. The paper's own spatially separated evaluation gives 1.78 m, a 14× jump, and that is the number that tells you what the system can do on unseen positions. The stress-test note is right, and the paper deserves credit for reporting the spatial split instead of burying it.

What is new: as far as I can tell, this is the first end-to-end demonstration of uplink SRS positioning over OAI with a commercial UE and standard E2SM-LLC telemetry. The engineering is non-trivial: making SRS/CFR extraction work through the E2 interface, aligning PDPs, computing phase features, and building a live dashboard. The feature design is physically sensible, and the RSSI-only baseline (0.2153 m) supports the claim that delay/phase structure helps — though that comparison also runs on the leaky random split, so it only shows in-distribution benefit.

Soft spots, in order of severity: 1. Evaluation protocol. The 80/20 random split over 23k frames from a dense grid almost certainly puts frames from the same grid point in both train and test. The model can memorize per-point fingerprints. The 1.78 m spatial split is the honest generalization number. The abstract and conclusion still headline 0.12 m and say 'sub-meter active positioning' without qualifying that it is within-grid interpolation. That framing should change. 2. 'Real-time' is asserted but not measured. No E2SM-LLC latency, no end-to-end latency; only ~1 s EMA lag is mentioned. The title makes a claim the evaluation does not support. 3. No error bars, no repeated runs, no confidence intervals. Single 6×6 m room. That is acceptable for a feasibility study, but it limits what you can conclude. 4. No code or data release. The demo video is not described in technical detail; for a reproducibility point, release would help a lot.

None of this makes the integration fake. The authors explicitly state that tree-based regressors struggle with extrapolation, which is the right acknowledgment. But the paper frames the interpolation result as the headline, and that is the load-bearing weakness.

Who benefits: researchers building O-RAN/ISAC localization prototypes, or anyone needing a reference architecture for getting SRS telemetry from OAI to a RIC xApp. It is a solid feasibility data point, not a new localization algorithm.

Recommendation: send to peer review. A good referee will force a corrected evaluation and proper latency measurement, and the paper will be stronger for it. If you only read one number, read 1.78 m, not 0.12 m.

Referee Report

3 major / 4 minor

Summary. The paper proposes an uplink SRS-based indoor localization system implemented on an OpenAirInterface (OAI) 5G testbed. A Positioning xApp on the Near-RT RIC extracts Channel Frequency Response (CFR) from SRS telemetry received via E2SM-LLC, converts it into a 32-dimensional physics-aware feature vector (PDP, inter-antenna phase differences, received power/spatial symmetry), and feeds it to a Random Forest regressor for 2D position estimation. Experiments in a 6×6 m multipath-rich lab with over 23,000 SRS frames report 0.12 m MAE under an 80/20 random split, 1.78 m MAE under a spatially separated blind split, and 0.2153 m MAE for an RSSI-only baseline. The paper also describes a real-time dashboard with EMA smoothing and multi-UE probability heatmaps, and claims sub-meter active positioning with real-time operation.

Significance. If the claims hold, the work provides a valuable integration of standardized E2SM-LLC SRS collection with a lightweight RF positioning pipeline, using commercial UE and an O-RAN testbed. The paper's strengths include the end-to-end system implementation, the physics-aware feature design, and the explicit disclosure of a spatially separated evaluation that degrades accuracy to 1.78 m. The latter control is scientifically informative, and the testbed contribution is useful to the O-RAN/ISAC community. However, the headline 0.12 m accuracy and the 'real-time' claim currently outrun the evidence, so the paper's significance depends on revising the claims to match the actual evaluation protocol.

major comments (3)

[Abstract and Sec. IV] The 0.12 m MAE is obtained under an 80/20 random split of 23,000 SRS frames collected on a dense grid in a 6×6 m room. With dense sampling, frames from the same physical location almost certainly appear in both training and test partitions, making this an interpolation metric rather than a test of localization of unseen positions. The paper's own spatially separated evaluation gives 1.78 m MAE, a 14× degradation, and this is disclosed. Nevertheless, the abstract and conclusion still headline 0.12 m and claim 'sub-meter active positioning.' This is a load-bearing mismatch between the claim and the evidence. The spatial-split result should be presented as the primary generalization metric, or all claims must be explicitly scoped to interpolation within the surveyed grid.
[Secs. II and IV] The title and abstract advertise 'real-time' localization, and Sec. II states that the mapping f:H→y operates under 'strict microsecond-level latency constraints.' However, no end-to-end latency measurement is provided. The only timing figure in the paper is the EMA filter's approximately 1 s lag, which is a visual smoothing artifact rather than a system latency bound. Without measuring E2SM-LLC delivery time, inference time, and end-to-end delay, the real-time claim is unsupported. Either add latency measurements or remove 'real-time' from the title/abstract and qualify the claim as near-real-time.
[Sec. IV (RSSI baseline and uncertainty)] The 42% improvement over the RSSI baseline (0.1238 vs. 0.2153 m) is computed under the same random split that leaks spatial information, so it does not independently validate generalization to new positions. In addition, no error bars, confidence intervals, or repeated-trial statistics are reported for any of the MAE values. Given the order-of-magnitude difference between the random-split and spatial-split results, the paper should report variance across multiple random seeds and also provide the RSSI baseline for the spatial split. This is necessary to assess whether the improvement is robust.

minor comments (4)

[Sec. IV / Fig. 3] The sentence stating 'approximately 85% of predictions within a 0.5 m error threshold' does not specify whether this refers to the random split or the spatial split. The CDF curves for both settings should be clearly labeled in Fig. 3 and in the text.
[Sec. II / Sec. IV] The reference to 'strict microsecond-level latency constraints' in Sec. II is not tied to any system requirement or measurement. Either substantiate it with a latency budget or remove it.
[Eq. (2)] The definition of the peak reference tap n_ref and the truncation to 16 taps is not fully specified. Please state how n_ref is selected and clarify whether the truncation is independent of the number of SRS subcarriers N.
[Sec. V] The claim that the lightweight pipeline is designed for 'rapid re-deployment in new environments' is plausible but not evaluated in the paper. Consider softening this claim or adding a brief discussion of the retraining cost.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the 0.12 m MAE is an empirical test-set metric, not a fitted constant masquerading as a prediction; the random-split leakage is an evaluation limitation disclosed by the paper, not equation-level circularity.

full rationale

The paper's derivation chain is CFR -> 32-dimensional physics-aware features (PDP, phase differences, received power) -> Random Forest regression -> 2D coordinates. Equations (1)-(4) transform measured channel data without reintroducing the target position y, and no fitted constant or trained parameter is renamed as a prediction. The headline 0.12 m MAE is an empirical evaluation result, not a quantity forced by construction. The random 80/20 split over dense grid frames is a genuine independence/generalization concern, but it is not circularity: the test frames are still measured outputs of the trained model, and the paper itself discloses the spatially separated split (1.78 m MAE) as a control. Self-citations to prior OAI/FlexRIC work and a demonstration video are background references, not load-bearing justifications, uniqueness theorems, or ansatz smuggling. No step reduces to its own inputs by definition, and no fitted parameter is presented as an independently predicted result. Therefore, no significant circularity is found.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on supervised learning from SRS-derived features. The free parameters are hand-set feature/hyperparameter choices, and the axioms are domain assumptions about channel-information sufficiency, evaluation independence, E2 telemetry fidelity, and environment stationarity. No new physical entities are introduced.

free parameters (4)

Random Forest hyperparameters = K=100, max_depth=12, min_samples_split=20
Hand-selected to balance accuracy and complexity; they directly shape the reported MAE and are not derived from theory.
PDP truncation length = 16 taps
Chosen as 'sufficient' to capture dominant multipath; the feature dimension and resulting accuracy depend on this hand-set value.
Antenna power calibration offset C_i = not reported
Equation (3) includes a hardware compensation offset per antenna; the values are not given, so the power features and reported error cannot be independently reproduced.
EMA filter lag = approximately 1 s
Applied to the dashboard output; it affects the real-time trajectory visualization claim but is not part of the MAE evaluation.

assumptions (4)

domain assumption SRS-derived CFR contains position-dependent multipath information sufficient for 2D regression
The entire approach assumes the 32-D features encode location; no model of SRS estimation noise, time/frequency offsets, or hardware imperfections beyond the C_i offset is given.
domain assumption An 80/20 random split over dense grid frames produces independent test locations
Frames from the same physical grid cell can appear in both train and test; the paper's spatial-split result (1.78 m) shows this assumption is questionable for generalization claims.
domain assumption E2SM-LLC streams SRS measurements to the xApp in real time with negligible loss or delay
Real-time claims depend on the E2 termination and LLC service model delivering CFR accurately; no latency, loss, or synchronization metrics are reported.
domain assumption The indoor environment and UE hardware are static during data collection
Fingerprinting models are trained in one lab under presumably static conditions; robustness to moving scatterers, channel changes over time, or different phones is not tested.

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Cite this review

Pith. "Pith review of Uplink SRS-Based Real-Time Indoor Localization System over OpenAirInterface." pith.science (2026). https://pith.science/paper/MYPHXJQ5

@misc{pith2026260718549,
  author       = {Pith},
  title        = {Pith review of: Uplink SRS-Based Real-Time Indoor Localization System over OpenAirInterface},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MYPHXJQ5}},
  note         = {Machine review of arXiv:2607.18549}
}

read the original abstract

Indoor localization is one of the important services for future 5G-Advanced and 6G systems. This paper presents an uplink Sounding Reference Signal (SRS)-based real-time indoor localization system implemented over an OpenAirInterface (OAI) 5G Radio Access Network (RAN). The proposed system uses a Positioning xApp to derive Channel Frequency Response (CFR) measurements from uplink SRS measurements. The SRS measurements are obtained from the gNB through the E2 Service Model for Lower Layer Control (E2SM-LLC) over the standardized E2 interface. The xApp transforms the CFR into a 32-dimensional physics-aware feature vector and uses a Random Forest (RF) regressor to estimate the two-dimensional position of the user equipment. We implemented the Positioning xApp on an OAI-based 5G testbed in a multipath-rich indoor laboratory at EURECOM to validate the proposed system. Experimental results show that the proposed system achieves a mean absolute error (MAE) of 0.12 m under random train-test evaluation. These results demonstrate the feasibility and limitations of uplink SRS-based real-time indoor localization over OAI.

Figures

Figures reproduced from arXiv: 2607.18549 by the authors.

**Figure 2.** Functional block diagram of the proposed Positioning xApp. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 4.** Experimental real-time trajectory tracking. Central open zones exhibit [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗

Figures from the paper (2 more)

**Figure 3.** Figure 3: Prediction error histogram and CDF under standard and spatial split [PITH_FULL_IMAGE:figures/full_fig_p003_3.png]

**Figure 5.** Figure 5: Real-time Multi-UE spatial tracking dashboard. Overlapping proba [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

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