Machine Learning for Disseminating Cooperative Awareness Messages in
Cellular V2V Communications
Abstract
This paper develops a novel Machine Learning (ML)-based strategy to
distribute aperiodic Cooperative Awareness Messages (CAMs) through
cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML
algorithm is employed by each vehicle to forecast its future CAM
generation times; then, the vehicle autonomously selects the radio
resources for message broadcasting on the basis of the forecast provided
by the algorithm. This action is combined with a wise analysis of the
radio resources available for transmission, that identifies subchannels
where collisions might occur, to avoid selecting them.
Extensive simulations show that the accuracy in the prediction of the
CAMs’ temporal pattern is excellent. Exploiting this knowledge in the
strategy for radio resource assignment, and carefully identifying idle
resources, allows to outperform the legacy LTE-V2X Mode 4 in all
respects.