An Unsupervised-Learning-Based Method for Multi-Hop Wireless Broadcast Relay Selection in Urban Vehicular Networks

Abstract

Multi-hop wireless broadcast is an important component of vehicular networks. Many services rely on the performance of broadcast communication to disseminate data packets. In an urban vehicular network, an efficient way of broadcasting data packets is choosing some vehicles as relay nodes. The base station only needs to multicast data packets to these relay vehicles and the packets would be spread to the whole network through D2D communication among vehicles. In this situation, the strategy of relay selection becomes a key factor of the broadcast efficiency. In this paper, we provide an unsupervised-learning-based method developed from k-means algorithm to help select relay nodes. The base station can learn the distribution of devices itself and choose the relay devices automatically. We use Manhattan map as our map model for the simulation and the result shows great efficiency over a random- selecting strategy.