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arXiv Machine Learning By Rasheed Bello, Arthur Mukwaya, Gurcan Comert, Varghese Vaidyan, Vijay Bendigeri, Anthony Dontoh, Jagruti Sahoo, Judith Mwakalonge

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

Read the original on arXiv Machine Learning →

NS3Learn is a closed‑form model that captures realistic 5G NR sidelink Mode‑2 reception losses—such as half‑duplex loss, scheduling collisions, receiver capture, and decoding—by fitting 10.5 million labeled outcomes from ns‑3 5G‑LENA traces. The model achieves a mean absolute deviation of 0.06 in per‑instant delivery compared to ns‑3, outperforming alternative models, and its parameters transfer with minimal error to new intersections. Using NS3Learn in traffic‑network simulations reverses traffic speed trends and more than doubles predicted hard‑braking events, demonstrating its impact on safety assessments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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