Channel Prediction Based Scheduling for Data Dissemination in VANETs

In vehicular ad hoc networks (VANETs), cooperative data dissemination is a promising approach addressing the problem of limited connection time between vehicles and road side units. However, existing data dissemination strategies rely too much on real-time channel state information, resulting in high communication overhead and transmission delay. To reduce the communication overhead and thus enhance the system throughput, we propose a channel prediction based scheduling strategy for data dissemination in VANETs. The channel prediction is built upon the recursive least squares algorithm. With low computational complexity, our proposed strategy achieves high scheduling efficiency verified by simulations of transmission delay and system throughput under both urban and highway scenarios.