Using Neural Network and Levenberg-Marquardt Algorithm for Link Adaptation Strategy in Vehicular Ad Hoc Network

Vehicular Ad Hoc Network (VANET) was initiated about two decades ago in view of saving lives by mitigating and reducing the number accidents incidents on public roads. Moreover, this objective can only be achieved if VANETs mobiles regularly exchange Road State Information (RSI) with their neighborhood take decisive actions based RSI received. Therefore, it becomes paramount to ensure that transmitted message is well And possible quality sharing medium or link controlled, transmission performed while taking into consideration Channel (CSI). The CSI provides information related channel quality, Signal-to-Noise Ratio (SNR), so forth. process adapting payload as a function called Link Adaptation (LA). Several LA works have already been published VANETs, but almost without serious effect relative mobility amongst nodes. Hence, Doppler Shift induced velocity, current work presents adaptation strategy using Neural (NN) Levenberg-Marquardt algorithm VANETs. simulation results definitively demonstrate NN approach outperforms its counterparts significant margin. It achieves performance 1075% duration, 180% bit, 115% model efficiency when compared Cte, ARF, AMC algorithms, respectively.