<![CDATA[Smart Transportation Systems (STS) leverage Vehicle-to-Vehicle (V2V) communication to enhance road safety, traffic efficiency, and urban mobility. However, ensuring reliable V2V communication remains challenging due to signal power instability, environmental interference, and scalability limitations. This study explores the optimization of V2V communication using Software Defined Radio (SDR) technology, which offers a cost-effective and adaptable approach for real-time signal processing. An SDR-based V2V communication system was developed using GNU Radio and HackRF One, with signal power calibration conducted through comparative measurements involving a Spectrum Analyzer across varying distances (3-15 meters) and environmental conditions. Performance evaluation focused on Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) under different vehicle speeds (20-40 km/h). Results indicate that increasing distance leads to signal degradation, with BER reaching 36.83% and SNR dropping to -3.17 dB, emphasizing the need for adaptive signal optimization techniques. While SDR-enabled calibration provided accuracy in signal measurements, environmental factors such as multipath interference and atmospheric attenuation significantly impacted communication reliability. Despite its flexibility, the system exhibited high BER and limited communication range, necessitating further enhancements through adaptive modulation schemes, machine learning-based power control, and hybrid 5G-DSRC integration. The study highlights SDR's potential for improving V2V communication while addressing key limitations in urban mobility networks. Future research should focus on enhancing scalability, security, and energy efficiency through advanced signal processing techniques. This study contributes to developing next-generation STS by providing empirical insights into SDR-based V2V communication optimization, supporting safer and more efficient transportation systems.]]>
Copyrights © 2025
