A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network
A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network
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DOI：
10.1109/icc40277.2020.9149319
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Rong Xia;Yong Xiao;Yingyu Li;M. Krunz;D. Niyato
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文献类型：
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作者：
Rong Xia;Yong Xiao;Yingyu Li;M. Krunz;D. Niyato
无线接入时延的时空建模对车联网系统具有重要意义。成型结果的质量在很大程度上依赖于样品的数量和质量，由于传感器的部署密度以及交通量和密度，样品的数量和质量会有很大的不同。本文提出了一种新的框架LaMI (Latency Model Inpainting)，用于生成跨大地理区域的联网车辆无线接入延迟的综合时空。LaMI采用了图像补图和合成的思想，通过两步重建缺失的延迟样本。特别是，它首先利用基于补丁的方法发现在不同区域收集的样本之间的空间相关性，然后将原始和高度相关的样本输入到深度生成模型变分自编码器（VAE）中，以创建与原始样本具有相似概率分布的延迟样本。最后，LaMI建立了延迟性能的经验PDF，并将PDF映射到不同车辆服务需求的置信水平。利用在大学校园的商用LTE网络中收集的真实迹线进行了广泛的性能评估。仿真结果表明，与现有的插值方法和基于最近邻的方法相比，该模型可以显著提高延迟建模的精度。
Spatio-temporal modeling of wireless access latency is of great importance for connected-vehicular systems. The quality of the molded results rely heavily on the number and quality of samples which can vary significantly due to the sensor deployment density as well as traffic volume and density. This paper proposes LaMI (Latency Model Inpainting), a novel framework to generate a comprehensive spatio-temporal of wireless access latency of a connected vehicles across a wide geographical area. LaMI adopts the idea from image inpainting and synthesizing and can reconstruct the missing latency samples by a two-step procedure. In particular, it first discovers the spatial correlation between samples collected in various regions using a patching-based approach and then feeds the original and highly correlated samples into a Variational Autoencoder (VAE), a deep generative model, to create latency samples with similar probability distribution with the original samples. Finally, LaMI establishes the empirical PDF of latency performance and maps the PDFs into the confidence levels of different vehicular service requirements. Extensive performance evaluation has been conducted using the real traces collected in a commercial LTE network in a university campus. Simulation results show that our proposed model can significantly improve the accuracy of latency modeling especially compared to existing popular solutions such as interpolation and nearest neighbor-based methods.