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Table 2 Machine learning in CR-VANETs (summarized from [[61]])

From: Cognitive radio for vehicular ad hoc networks (CR-VANETs): approaches and challenges

Machine learning tools

Application

Case based reasoning

Faster convergence from exploiting previous

 

solutions for example when traveling through

 

same area on same day of the week

Support vector machine

Signal classification and identifying

and neural networks

spectrum holes, channel prediction

Reinforcement learning

Channel selection, real-time learning, can

 

be used in unknown environments

Genetic algorithms and

Optimization of transceiver

simulated annealing

parameters