NS3Learn：将5G NR Mode-2接收真实性从ns-3迁移至Veins/SUMO栈用于网联车辆安全评估
NS3Learn: Transferring 5G NR Mode-2 Reception Realism from ns-3 to the Veins/SUMO Stack for Connected-Vehicle Safety Assessment
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中文总结 AI 辅助
本研究提出NS3Learn闭式模型，通过蒸馏ns-3仿真数据，在Veins/SUMO栈中准确模拟5G NR Mode-2资源竞争，显著提升密集交通下网联车辆安全评估的真实性。
中文摘要 AI 辅助
网联车辆安全评估依赖于耦合的交通与网络仿真，但标准信道模型忽略了5G NR侧行链路Mode-2中的无线资源竞争，在密集交通中报告了不切实际的高消息投递率。本研究在不需完整协议重实现的情况下引入了资源竞争损失。我们从ns-3 5G-LENA轨迹（基于3GPP场景校准并由SUMO轨迹驱动）中标注了1050万个接收结果，以拟合NS3Learn——一个捕获半双工损失、调度碰撞、接收端捕获和解码的闭式模型。评估覆盖了两个信号化城市网络、六个渗透率水平（1-100%）以及每个条件下的五个随机种子。NS3Learn在每时刻投递率上与ns-3 5G-LENA相比实现了0.06的平均绝对偏差，优于替代模型（偏差分别为0.44和0.55）。拟合参数迁移至一个不同的交叉口，仅增加了20%的额外误差。关键的是，使用真实通信模型逆转了模拟交通速度趋势，并将预测的急刹车事件数量增加了一倍以上。该框架通过模型蒸馏而非完全重实现，在仿真器之间迁移接收真实性。每个阶段都直接对应一个明确的物理机制。研究人员和交通机构可以在保持现有仿真流程的同时，准确考虑密集交通下的数据包丢失和拒绝服务影响。适应新的无线配置仅需离线重新拟合，而无需修改代码。
英文摘要
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.
发表机构
- South Carolina State University（南卡罗来纳州立大学）
- North Carolina A&T State University（北卡罗来纳农工州立大学）
- Dakota State University（达科他州立大学）
机构由 AI 辅助整理，请以论文原文为准。
