Model-Tree-Based Rate Adaptation Scheme for Vehicular Networks

Rate adaptation techniques have been extensively studied for traditional wireless LANs as way to adjust the data transmission bandwidth as a function of the channel quality. However, existing solutions which are mostly based on statistics collection inherently experience a large delay in response to the wireless channel fluctuations, which is unsuitable for the rapid changing of the channel conditions in vehicular networks. In this paper, we propose an efficient self-adaptive model-tree-based rate adaptation scheme termed MTRA that can predict the packet error rate (PER) and adapt the data rate in real time. We also present a detailed methodology for the PER model tree building and update. We have performed comprehensive experimentations using ns-2 simulations which demonstrate that MTRA can achieve much better performance than the traditional rate adaptation approaches under various scenarios in vehicular networks.