6G AND V2X: REVOLUTIONING CONNECTED  VEHICLE NETWORKS

Author(s)	Mr. Rapuru Venkatesh Venky, Mr. Pydisetty Vidhya Praveen, Dr. R. Pandian
Country	India
Abstract	Vehicle-to-Vehicle (V2V) communication isessential for improving road safety and optimizing trafficflow. Machine learning techniques, such as Support VectorMachines (SVM), offer an efficient method for estimatingcommunication reliability in real-time without requiring acomplete network model. In this study, Ant ColonyOptimization (ACO) was applied to enhance the trainingprocess of an SVM model for classifying V2V messagereliability. The model underwent ten-fold crossvalidationbefore being implemented using Verilog HardwareDescription Language (VHDL) for FPGA execution,enabling a cost-effective and efficient hardware solution. Byutilizing a linear SVM, the system achieved low powerconsumption, using only 1.4% of the Xilinx Artix7 FPGA’sresources. This efficiency allows multiple instances of thereliability estimation model to operate simultaneously,facilitating the monitoring of multiple vehicle links.
Keywords	6G Communication, V2X, V2V, Machine Learning for V2X, Support Vector Machines, Ant Colony Optimization, Reliability Estimation, Real-Time Communication, FPGA Implementation, Verilog HDL, Xilinx Artix7 FPGA
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-05
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.40405
Short DOI	https://doi.org/g9dgzs

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