Joint Communication and Sensing (JCAS) for Vehicular Networks
DOI:
https://doi.org/10.15662/IJRAI.2024.0704002Keywords:
Joint Communication and Sensing (JCAS), Vehicular Networks, Intelligent Transportation Systems (ITS), Autonomous Vehicles, Spectrum Sharing, Signal Processing, Waveform Design, Sensor Fusion, Wireless Communication, 5G and BeyondAbstract
Joint Communication and Sensing (JCAS) is an emerging paradigm integrating wireless communication and sensing functionalities within a unified framework, which has gained significant attention for vehicular networks. The increasing demand for intelligent transportation systems (ITS) necessitates enhanced situational awareness, low latency, and reliable communication. JCAS offers a promising solution by enabling vehicles to simultaneously communicate and sense their environment using shared spectrum and hardware resources. This dual-functionality reduces system complexity, saves spectrum, and improves the efficiency of vehicular networks. This paper reviews the fundamental concepts, enabling technologies, and challenges associated with JCAS in vehicular networks. We discuss the critical role of JCAS in supporting advanced applications such as autonomous driving, collision avoidance, and traffic management. Various signal processing techniques, waveform designs, and hardware architectures are examined to understand their impact on the performance of vehicular JCAS systems. We also present a comparative analysis of existing JCAS schemes, emphasizing their communication reliability, sensing accuracy, and resource efficiency. Furthermore, the paper highlights the research gaps and open issues, including interference management, hardware constraints, and standardization efforts. Experimental results from recent studies demonstrate that JCAS can achieve significant improvements in spectral efficiency and sensing accuracy compared to separate communication and sensing systems. Finally, this work outlines potential future directions for integrating artificial intelligence and machine learning with JCAS to enhance adaptability and robustness in dynamic vehicular environments. The insights provided in this paper aim to guide researchers and industry practitioners in designing efficient JCAS-enabled vehicular networks for nextgeneration intelligent transportation systems.
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