RFRA: Random Forests Rate Adaptation for vehicular networks
Oscar Puñal, Hanzhi Zhang, James Gross · 2013
Rate adaptation in vehicular networks is known to be more challenging than in WLANs due to the high mobility of stations. Nevertheless, vehicular networks are subject to certain recurring patterns particularly if stations communicate to roadside units. This has lead to the proposal of learning-based rate adaptation schemes which are trained for a certain propagation environment. In general, these schemes outperform other approaches at the price of being specific for a particular environment. In this paper we present RFRA, a novel rate adaptation scheme for vehicular networks. It is based on the machine-learning algorithm Random Forests which is known to be superior to most other learning approaches. Firstly, we show that RFRA outperforms other learning-based methods significantly. We also study the question how sensitive RFRA is to changes of the learned environment, especially with respect to the propagation characteristics. We show that, although this reduces the gain of our scheme, RFRA still provides a much higher performance than state-of-the-art rate adaptation schemes.