Supervised Learning Technique for First Order Multipaths Identification of V2V Scenario

• Main Authors: A. Bakhuraisa, Yaser A., Abd Aziz, Azlan, Tan K. Geok, Tan K. Geok, Abu Bakar, Norazhar, Jamian, Saifulnizan, Mustakim, Fajaruddin
• Format: Article
• Language: English
• Published: Mdpi 2023
• Subjects: QA101-(145) Elementary mathematics. Arithmetic
• Online Access: http://eprints.uthm.edu.my/9970/1/J16027_71142911d6f767fe3ef560a9043a8db7.pdf; http://eprints.uthm.edu.my/9970/; https://doi.org/10.3390/wevj14040109

geometrical localization techniques, the propagated signal’s first-order multipath (FOMP) characteristics are used to calculate the location based on geometrical relationships. Utilizing the characteristics of higher order multipath (HOMP) results in a significant localization error. Therefore, distinguishing between FOMPs and HOMPs is an important task. The previous works used traditional methods based on a deterministic threshold to accomplish this task. Unfortunately, these methods are complicated and insufficiently accurate. This paper proposes an efficient method based on supervised learning to distinguish more accurately between the propagated FOMP and HOMP of millimeter-Wave Vehicle-to-Vehicle communication in an urban scenario. Ray tracing technique based on Shoot and Bounce Ray (SBR) is used to generate the dataset’s features including received power, propagation time, the azimuth angle of arrival (AAOA), and elevation angle of arrival (EAOA). A statistical analysis based on the probability distribution function (PDF) is presented first to study the selected features’ impact on the classification process. Then, six supervised classifiers, namely Decision Tree, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Random Forest, and artificial neural network, are trained and tested, and their performance is compared in terms of HOMP misclassification. The effect of the considered features on the classifiers’ performance is further investigated. Our results showed that all the proposed classifiers provided an acceptable classification performance. The proposed ANN showed the best performance, whereas the NB was the worst. In fact, the HOMP misclassification error varied between 2.3% and 16.7%. The EAOA exhibited the most significant influence on classification performance, while the AAOA was the least.