Mohammad Yanuar Hariyawan, Hendy Briantoro

Improving V2V Communication Reliability in Dynamic Vehicular Networks: A Software-Defined Radio-Based Approach

Introduction

Improving v2v communication reliability in dynamic vehicular networks: a software-defined radio-based approach. Optimize V2V communication in dynamic vehicular networks using a Software Defined Radio (SDR) approach. This study enhances road safety, traffic efficiency, and urban mobility, detailing performance, limitations, and future enhancements for next-gen STS.

Abstract

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.

Review

This study critically examines the application of Software-Defined Radio (SDR) technology to enhance the reliability of Vehicle-to-Vehicle (V2V) communication within dynamic vehicular networks, a crucial component for Smart Transportation Systems. The authors address significant challenges such as signal instability, environmental interference, and scalability limitations inherent in current V2V communication. By developing an SDR-based system using GNU Radio and HackRF One, the research explores a flexible and cost-effective approach for real-time signal processing. The methodology involved meticulous signal power calibration through comparative measurements with a Spectrum Analyzer across varying distances and environmental conditions, followed by performance evaluation focused on Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) under different vehicle speeds. The findings from the experimental evaluation provide valuable empirical insights, albeit highlighting significant current limitations. The study clearly demonstrates that increasing communication distance leads to considerable signal degradation, with BER reaching 36.83% and SNR dropping to -3.17 dB, underscoring the formidable challenges posed by dynamic vehicular environments. While the SDR-enabled calibration proved effective in ensuring accurate signal measurements, environmental factors such as multipath interference and atmospheric attenuation were identified as critical determinants of communication reliability. Despite SDR's inherent flexibility, the current system exhibited high BER and a notably limited communication range. This necessitates the integration of more sophisticated techniques, including adaptive modulation schemes, machine learning-based power control, and hybrid 5G-DSRC solutions, to overcome these performance bottlenecks. In conclusion, this research makes a valuable contribution by providing a foundational empirical assessment of SDR's potential for V2V communication optimization, supporting the development of safer and more efficient transportation systems. Despite the current system's limitations in terms of high BER and constrained range, the study effectively highlights SDR's adaptability and cost-effectiveness as a promising platform for future advancements. The identified need for enhanced scalability, security, and energy efficiency, coupled with the recommendation for advanced signal processing techniques, sets a clear agenda for subsequent research. This work serves as an important step towards realizing the full potential of next-generation Smart Transportation Systems, laying the groundwork for more robust and reliable V2V communication architectures.

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