  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.
