Abstract

Recently, 5G and beyond 5G (B5G) systems, Ultrareliable Low Latency Network (URLLC) represents the key enabler for a range of modern technologies to support Industry 4.0 applications, such as transportation and healthcare. Real-world implementation of URLLC can help in major transformations in industries like autonomous driving, road safety, and efficient traffic management. Furthermore, URLLC contributes to the objective of fully autonomous cars on the road that can respond to dynamic traffic patterns by collaborating with other vehicles and surrounding environments rather than relying solely on local data. For this, the main necessity is that how information is to be transferred among the vehicles in a very small time frame. This requires information to be transferred among the vehicles reliably in extremely short time duration. In this paper, we have implemented and analyzed the Multiaccess Edge Computing- (MEC-) based architecture for 5G autonomous vehicles based on baseband units (BBU). We have performed Monte Carlo simulations and plotted curves of propagation latency, handling latency, and total latency in terms of vehicle density. We have also plotted the reliability curve to double-check our findings. When the RSU density is constant, the propagation latency is directly proportional to the vehicle density, but when the vehicle density is fixed, the propagation latency is inversely proportional. When RSU density is constant, vehicle density and handling latency are strictly proportional, but when vehicle density is fixed, handling latency becomes inversely proportional. Total latency behaves similarly to propagation latency; that is, it is also directly proportional.

Highlights

  • Connecting vehicles with each other and the infrastructure around them could play a significant role in autonomous vehicles’ future and improving safety

  • E major contributions of this paper are as follows: (i) Analysis of the literature, identification of challenges of the existing works, and techniques to resolve them (ii) Examination of the architecture based on mobile edge computing (MEC) running together with Virtualized Radio Access Network services on Edge Servers improving the efficiency and security of autonomous vehicles and enabling a 5G-based Ultrareliable Low Latency Network (URLLC) networks for smart Internet of Vehicles (IoVs)

  • The reliability and convergence have been plotted. e graph shows the boundation imposed on the remote queues on their average long-term lengths. e probability at which the sum of the queue lengths increases is a predefined threshold. e challenges are resolved using a dynamic algorithmic framework that is solved using optimization without having a prerequisite knowledge of the radio channel data arrivals. rough these graphs, we can interpret a fast-converging behaviour and the capacity of the system to adapt in the nonstationary environment

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Summary

Introduction

Connecting vehicles with each other and the infrastructure around them could play a significant role in autonomous vehicles’ future and improving safety. E proposed general 5G infrastructure is built on the interconnection of numerous new technologies, such as Massive MIMO networks, Cognitive Radio Networks, and Mobile and Static Cell Networks [9] Traditional performance metrics, such as spectral quality and network bandwidth, must be increased due to the continued advancement of 5G technologies, and a wide range of connectivity modes must be offered to increase customer experience [9]. (i) Analysis of the literature, identification of challenges of the existing works, and techniques to resolve them (ii) Examination of the architecture based on mobile edge computing (MEC) running together with Virtualized Radio Access Network (vRAN) services on Edge Servers improving the efficiency and security of autonomous vehicles and enabling a 5G-based URLLC networks for smart Internet of Vehicles (IoVs).

Related Work
Research Significance
Combination of MEC and MCC
MEC-Based BBU Architecture for Autonomous Vehicles
Results and Discussion
Limitations and Future
Conclusion
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