H-V2X Mode 4 Adaptive Semipersistent Scheduling Control for Cooperative Internet of Vehicles

Heo, Sihun; Yoo, Wonsuk; Jang, Hyeondeok; Chung, Jong-Moon

doi:10.1109/jiot.2020.3048993

Cited by 32 publications

(11 citation statements)

References 25 publications

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“…The authors in [22] and [23] propose reserving resources at each resource selection instance and alternately using them to mitigate the collision probability. Other works propose exchange of information among the vehicles, such as channel measurements [24], location [25], or status and reservation information of resources [26], [27], [28]. Revisions to the sensing mechanism are also proposed in several works, mainly by considering different weighting strategies for selecting the resources [29], [30].…”

Section: Distributed Scheduler Lte Modementioning

confidence: 99%

Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning

Şahin

Khalili

Boban

et al. 2022

IEEE Trans. Veh. Technol.

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Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliability, their operation is conventionally restricted to areas with full cellular network coverage. In contrast, in outof-cellular-coverage areas, comparatively inefficient distributed radio resource management is used. To exploit the benefits of the centralized approach for enhancing the reliability of V2V communications on roads lacking cellular coverage, we propose VRLS (Vehicular Reinforcement Learning Scheduler), a centralized scheduler that proactively assigns resources for out-of-coverage V2V communications before vehicles leave the cellular network coverage. By training in simulated vehicular environments, VRLS can learn a scheduling policy that is robust and adaptable to environmental changes, thus eliminating the need for targeted (re-)training in complex real-life environments. We evaluate the performance of VRLS under varying mobility, network load, wireless channel, and resource configurations. VRLS outperforms the state-of-the-art distributed scheduling algorithm in zones without cellular network coverage by reducing the packet error rate by half in highly loaded conditions and achieving near-maximum reliability in low-load scenarios.

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Section: Distributed Scheduler Lte Modementioning

confidence: 99%

Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning

Şahin

Khalili

Boban

et al. 2022

IEEE Trans. Veh. Technol.

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“…In [105], the authors suggest to introduce the exchange of local measurements. Based on that, the VUEs estimate the collision probability and possibly modify the packet transmission rate accordingly; a backup to the DSRC interface in a separate band is also foreseen if the required QoS cannot be granted.…”

Section: ) Modifications To the Sb-sps Proceduresmentioning

confidence: 99%

On the Design of Sidelink for Cellular V2X: A Literature Review and Outlook for Future

et al. 2021

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Connected and fully automated vehicles are expected to revolutionize our mobility in the near future on a global scale, by significantly improving road safety, traffic efficiency, and traveling experience. Enhanced vehicular applications, such as cooperative sensing and maneuvering or vehicle platooning, heavily rely on direct connectivity among vehicles, which is enabled by sidelink communications. In order to set the ground for the core contribution of this paper, we first analyze the main streams of the cellular-vehicle-to-everything (C-V2X) technology evolution within the Third Generation Partnership Project (3GPP), with focus on the sidelink air interface. Then, we provide a comprehensive survey of the related literature, which is classified and critically dissected, considering both the Long-Term Evolutionbased solutions and the 5G New Radio-based latest advancements that promise substantial improvements in terms of latency and reliability. The wide literature review is used as a basis to finally identify further challenges and perspectives, which may shape the C-V2X sidelink developments in the next-generation vehicles beyond 5G.INDEX TERMS 5G, Cellular-vehicle-to-everything, literature review, new radio, sidelink.

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“…Internet of Vehicles (IoVs) is a major application of IoT in the transportation industry [33][34][35][36][37][38]. IoVs involve vehicles that are equipped with wireless transceivers and infrastructure Road Side Units (RSUs) placed at different regions on the road.…”

Section: Internet Of Vehiclesmentioning

confidence: 99%

Reliable Internet of Things: Challenges and Future Trends

et al. 2021

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The Internet of Things (IoT) is a vital component of many future industries. By intelligent integration of sensors, wireless communications, computing techniques, and data analytics, IoT can increase productivity and efficiency of industries. Reliability of data transmission is key to realize several applications offered by IoT. In this paper, we present an overview of future IoT applications, and their major communication requirements. We provide a brief survey of recent work in four major areas of reliable IoT including resource allocation, latency management, security, and reliability metrics. Finally, we highlight some of the important challenges for reliable IoT related to machine learning techniques, 6G communications and blockchain based security that need further investigation and discuss related future directions.

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H-V2X Mode 4 Adaptive Semipersistent Scheduling Control for Cooperative Internet of Vehicles

Cited by 32 publications

References 25 publications

Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning

Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning

On the Design of Sidelink for Cellular V2X: A Literature Review and Outlook for Future

Reliable Internet of Things: Challenges and Future Trends

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