Self-Adaptive Clustering and Load-Bandwidth Management for Uplink Enhancement in Heterogeneous Vehicular Networks

Due to the diversity of traffic scenes and high mobility of vehicles, the propagation environment between moving vehicles and road side access points can be highly dynamic, which causes unstable uplink connectivity and time-varying uplink data rates in vehicular networks. In this paper, we study the uplink performance of heterogenous vehicular networks, which integrate dedicated short-range communications (DSRCs) and Long Term Evolution vehicle-to-everything (LTE V2X) into a single vehicular network to provide high reliability, low latency, and wide area coverage. Here, we adopt a cluster-based approach, in which DSRC and LTE are utilized to provide vehicle-to-vehicle communications between vehicles within a cluster and vehicle-to-infrastructure communications between vehicles and access points, respectively. Specifically, a self-adaptive clustering method is proposed based on the iterative self-organizing data analysis technique algorithm, in which the number of clusters can automatically adjust to the optimal value according to the mobility information. Also, a joint load-bandwidth management scheme is proposed to distribute traffic load and bandwidth resources between DSRC and LTE. Simulation results show that the proposed algorithm outperforms the traditional section-based and ${K}$ -means clustering methods, and a tradeoff between average uplink data rate and signaling overhead can be achieved.