Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks

• URL: http://arxiv.org/abs/2103.07636v1
• Date: Sat, 13 Mar 2021 06:56:29 GMT
• Title: Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks
• Authors: Juntong Liu, Yong Xiao, Yingyu Li, Guangming Shiyz, Walid Saad, and H. Vincent Poor
• Abstract summary: A graph-based framework called SMART is proposed to model and keep track of the statistics of vehicle-to-temporal (V2I) communication latency across a large geographical area. We develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm. Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and the latency performance of large vehicular networks.
• Score: 110.80088437391379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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