Search arXiv⌕ Search

arXiv · 2610.20387

Modeling Diffuse Scattering in Mobility Scenarios: Ray Tracing Model and Measurement Validation

Abstract

Open-source ray tracing (RT) frameworks are increasingly used for site-specific channel modeling due to their reproducibility and extensibility. In non-stationary mobility scenarios, repeated RT evaluations can generate different diffuse propagation paths without preserving their correspondence, limiting a consistent representation of their temporal evolution. To address this limitation, we extend diffuse scattering modeling in Sionna RT using persistent surface-associated scattering points. Their positions remain fixed, while their contributions evolve according to changes in the propagation geometry and visibility. The proposed approach is validated using empirical radio channel impulse response measurements in an urban vehicular scenario at 5.9 GHz. Compared with the standard Sionna RT baseline, the proposed model shows improved agreement with measurements in the delay and Doppler domains.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Milica Djordjevic, Thomas Wilding, Anja Dakić, Markus Hofer, Benjamin Rainer, Thomas Zemen. 2026-10-14. Modeling Diffuse Scattering in Mobility Scenarios: Ray Tracing Model and Measurement Validation. https://arxiv.org/abs/2610.20387

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Denoising Rather Than Gap Filling: Missing-Data Handling in Sparse Outdoor BLE Positioning

Received signal strength indicator (RSSI) positioning outdoors has one to two orders of magnitude fewer anchors than the indoor systems its methods come from. In the ten-day cattle-tracking deployment reported here, four gateways cover $4{,}302\,\mathrm{m}^2$, or $0.93$ anchors per $1000\,\mathrm{m}^2$. An animal is heard by $0.99$ gateways per second on average, so the observation vector for a given second is almost never complete. How the gaps are filled is therefore a first-order design choice, not a preprocessing detail. Filling at all is worth $8.1\,\mathrm{m}$, $96\%$ of the improvement the hold-length setting can deliver and a $29\%$ error reduction. How long a value is then held is worth the remaining $4\%$, from four seconds to unbounded. Once a value is available, the gain comes from removing noise rather than rebuilding the lost sample: a filtered channel estimate improves on a held raw sample, whereas filling backwards from future samples makes it worse. As that mechanism predicts, smoothing strength has a real interior optimum that is costly to miss in either direction. A smoother allowed to read the future gains only $0.55\,\mathrm{m}$, one fifteenth of what filling is worth, which bounds what any offline method can add. Three method families do not apply here for structural reasons rather than poor performance: two or more of the four channels are live in only $25\%$ of seconds, so the cross-channel structure generative imputation must learn is largely unobserved.

eess.SP↗

Centroid Angle Estimation of Multiple Scatterers Using Monopulse Radar with Frequency Diversity

The monopulse technique determines the angle of a target by comparing signals from two narrow beams, yielding a precise angular estimate with low complexity. However, it struggles to resolve multiple closely spaced scatterers within the same resolution cell. Existing methods for estimating multiple scatterer angles involve complex signal processing and system modifications. We propose an effective method to estimate the angular centroid of scatterers using the mode of monopulse angle estimates. A semi-analytic expression for the angle estimate distribution is derived, confirming that its mode aligns with the centroid. To enhance estimation accuracy, we employ frequency diversity to reduce sample correlation. Numerical results validate the advantages of the proposed method, demonstrating superior performance over conventional techniques with low complexity.

eess.SP↗

Sensing-Aided Vehicular Beam Tracking: System Gain Over Link Feedback and Probe-Verified Sensing Invocation

Sensor-based beam prediction is usually evaluated against exhaustive beam training, leaving open when sensing adds useful information beyond a deployed tracker's link feedback and when it should be withheld. We separate system gain from the marginal value of sensing in action-restricted causal replay of recorded 64-beam sweeps from four vehicular DeepSense 6G scenarios. The replay exposes only serving-beam and explicitly probed candidate powers and counts probes and model invocations separately. The proposed controller combines a scheduled post-setup re-sweep with probe-verified sensing admission and blacklist backoff. In three scenarios where feedback-only tracking is within 0.25 dB of the recorded-sweep oracle, the controller stays close to feedback-only in mean loss while rarely invoking the model, whereas always-on sensing performs worse. In the sole high-innovation scenario, the controller's system gain is 0.82 dB (95% confidence interval [0.30, 1.40]) over the plain feedback-only tracker and 0.15 dB over a probe-limited re-sweeping reference, with fewer probes than either. Sensing and the re-sweep both correct setup lock loss. At the native decision interval, enabling sensing in the same controller yields no statistically resolved loss reduction but cuts probing by 21%. In this scenario, accurate sensing reduces loss relative to the sensor-disabled controller under emulated link noise, staleness or longer decision intervals. Weak or delayed sensing can instead increase loss despite probe verification. These exploratory results characterize sensing as a conditional substitute for RF probing or a source of loss reduction, depending on the limitations of link feedback and on the accuracy and timeliness of sensing.

eess.SP↗