# NS3Learn: Transferring 5G NR Mode-2 Reception Realism from ns-3 to the Veins/SUMO Stack for Connected-Vehicle Safety Assessment

arXiv: https://arxiv.org/abs/2609.20578 · HTML: https://mlsanity.com/paper/2609.20578/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [Spatio-temporal Dynamics of Cellular V2X Communication in Dense Vehicular Networks](https://mlsanity.com/paper/1906.08634/) (2019)
- [New Radio Physical Layer Abstraction for System-Level Simulations of 5G Networks](https://mlsanity.com/paper/2001.10309/) (2020)
- [Calibration of the 5G-LENA System Level Simulator in 3GPP reference scenarios](https://mlsanity.com/paper/2205.03278/) (2022)
- [Analytical Models of the Performance of C-V2X Mode 4 Vehicular Communications](https://mlsanity.com/paper/1807.06508/) (2018)
- [An E2E Simulator for 5G NR Networks](https://mlsanity.com/paper/1911.05534/) (2019)
- [OpenCV2X: Modelling of the V2X Cellular Sidelink and Performance Evaluation for Aperiodic Traffic](https://mlsanity.com/paper/2103.13212/) (2021)
- [A Methodology for Abstracting the Physical Layer of Direct V2X Communications Technologies](https://mlsanity.com/paper/2203.17114/) (2022)
- [Toward 6G Sidelink Reliability: MAC PRR Modeling for NR Mode 2 SPS and ns-3 Validation](https://mlsanity.com/paper/2309.16680/) (2023)
- [Analytical Model of NR-V2X Mode 2 with Re-Evaluation Mechanism](https://mlsanity.com/paper/2510.27108/) (2025)
- [Analyzing the Impact of Adversarial Attacks on C-V2X-Enabled Road Safety: An Age of Information Perspective](https://mlsanity.com/paper/2603.03462/) (2026)

## Abstract (arXiv)

Connected-vehicle safety evaluations rely on coupled traffic and network simulations, but standard channel models ignore radio resource competition in 5G NR sidelink Mode-2, reporting unrealistically high message delivery in dense traffic. This study introduces resource-competition losses without requiring full protocol reimplementation. We labeled 10.5 million reception outcomes from ns-3 5G-LENA traces (calibrated on 3GPP scenarios and driven by SUMO trajectories) to fit NS3Learn - a closed-form model capturing half-duplex loss, scheduling collisions, receiver capture, and decoding. Evaluation spanned two signalized urban networks, six penetration levels (1-100%), and five random seeds per condition. NS3Learn achieved a mean absolute deviation of 0.06 in per-instant delivery compared to ns-3 5G-LENA, outperforming alternative models (0.44 and 0.55 deviation). Fitted parameters transferred to a distinct intersection with only 20% additional error. Crucially, using realistic communication models reversed simulated traffic speed trends and more than doubled predicted hard-braking events. The framework transfers reception realism between simulators via model distillation instead of full reimplementation. Every stage maps directly to an explicit physical mechanism. Researchers and transportation agencies can maintain existing simulation pipelines while accurately accounting for dense-traffic packet loss and denial-of-service impacts. Adapting to new radio configurations requires only offline refitting rather than code modification.