  The effective deployment of connected vehicular networks is contingent upon
maintaining a desired performance across spatial and temporal domains. In this
paper, a graph-based framework, called SMART, is proposed to model and keep
track of the spatial and temporal statistics of vehicle-to-infrastructure (V2I)
communication latency across a large geographical area. SMART first formulates
the spatio-temporal performance of a vehicular network as a graph in which each
vertex corresponds to a subregion consisting of a set of neighboring location
points with similar statistical features of V2I latency and each edge
represents the spatio-correlation between latency statistics of two connected
vertices. Motivated by the observation that the complete temporal and spatial
latency performance of a vehicular network can be reconstructed from a limited
number of vertices and edge relations, we develop a graph reconstruction-based
approach using a graph convolutional network integrated with a deep Q-networks
algorithm in order to capture the spatial and temporal statistic of feature map
pf latency performance for a large-scale vehicular network. Extensive
simulations have been conducted based on a five-month latency measurement study
on a commercial LTE network. Our results show that the proposed method can
significantly improve both the accuracy and efficiency for modeling and
reconstructing the latency performance of large vehicular networks.
