Machine learning for efficient link adaptation strategy in VANETs

The benefit brought by Vehicular Ad Hoc Networks (VANETs) can only be gained if the successful Road State Information (RSI) message notifications are exchanged between the mobiles involved. Moreover, a successful exchange is only possible with a well-integrated Link Adaptation (LA) mechanism. Furthermore, the higher mobility induces Doppler Shift (DS) in the carrier frequency component of the transmitter node, which corrupts the transmitted signal and makes decoding difficult at the receiver end. Several authors have addressed the LA in VANETs, but almost all of them have done so without incorporating an effective DS mitigation strategy. The current study presents a Machine Learning (ML) approach for an efficient LA strategy in VANETs. The simulation results demonstrated that the ML outperformed AMC, ARF and Cte in threefold, with an improvement level of 212% in terms of throughput, 86.5% in terms of transmission duration and 39% in terms of model efficiency. Copyright © 2023 Inderscience Enterprises Ltd.