Revolutionizing Intelligent Transportation Systems with Cellular Vehicle-to-Everything (C-V2X) technology: Current trends, use cases, emerging technologies, standardization bodies, industry analytics and future directions

Vehicular Communications, Journal Year: 2023, Volume and Issue: 43, P. 100638 - 100638

Published: July 11, 2023

Language: Английский

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Network Slicing Based Learning Techniques for IoV in 5G and Beyond Networks

IEEE Communications Surveys & Tutorials, Journal Year: 2024, Volume and Issue: 26(3), P. 1989 - 2047

Published: Jan. 1, 2024

The effects of transport development on people's lives are diverse, ranging from economy to tourism, health care, etc. Great progress has been made in this area, which led the emergence Internet Vehicles (IoV) concept. main objective concept is offer a safer and more comfortable travel experience through making available vast array applications, by relying range communication technologies including fifth-generation mobile networks. proposed applications have personalized Quality Service (QoS) requirements, raise new challenging issues for management allocation resources. Currently, interest doubled with start discussion sixth-generation In context, Network Slicing (NS) was presented as one key 5G architecture address these challenges. article, we try bring together NS implications field show impact development. We begin reviewing state art IoV terms architecture, types, life cycle, enabling technologies, network parts, evolution within cellular Then, discuss benefits brought use such dynamic environment, along technical Moreover, provide comprehensive review deploying various aspects Learning Techniques Vehicles. Afterwards, present utilization different application scenarios domains; terrestrial, aerial, marine. addition, Vehicle-to-Everything (V2X) datasets well existing implementation tools; besides presenting concise summary Slicing-related projects that an IoV. Finally, order promote deployment IoV, some directions future research work. believe survey will be useful researchers academia industry. First, acquire holistic vision regarding IoV-based realization identify challenges hindering it. Second, understand progression powered fields (terrestrial, marine). determine opportunities using Machine (MLT), propose their own solutions foster NS-IoV integration.

Language: Английский

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Critical Review of Vehicle-to-Everything (V2X) Topologies: Communication, Power Flow Characteristics, Challenges, and Opportunities

CPSS Transactions on Power Electronics and Applications, Journal Year: 2024, Volume and Issue: 9(1)

Published: March 1, 2024

The rise in demand for energy storage solutions and the widespread adoption of electric vehicles (EVs) have given to creation vehicle-to-everything (V2X) topologies.V2X technology enables communication power flow between EVs, grid, homes, buildings, other loads.This paper provides an acute review V2X topologies, including EVs vehicles, loads.The different types communication, IEEE standards, 3rd Generation Partnership Project (3GPP), ISO Wi-Fi, Internet Things (IoT)-based protocols, are discussed, along with their advantages disadvantages.Finally, challenges opportunities topologies presented.Index Terms-5G, C-V2X, DSRC, internet things, vehicle home, vehicle. I. IntroductIonT HE advent has caused a substantial shift transportation industry, offering possibility mitigate greenhouse gas emanations lessen reliance on non-renewable sources.However, integration into grid offers new system.vehicle-to-everything is one promising that allows bi-directional enabling integrate support grid's stability reliability.This includes several can provide various services, vehicle-to-grid (V2G), vehicle-tohome (V2H), building (V2B), load (V2L), (V2V) [1].V2G ability store supply during high hours, while V2B V2H allow buildings homes outages or reduce consumption

Language: Английский

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5G-Enabled V2X Communication with Cloud-Powered XGBoost Algorithm for Electric Transportation

Published: April 12, 2024

The opportunity for electric transportation system optimization has never been greater with the combination of cloud-based machine learning algorithms and 5G-enabled Vehicle-to-Everything (V2X) connectivity. To improve effectiveness, reliability, longevity vehicle (EV) networks, it provides a new architecture combining V2X communication enabled by 5G an XGBoost algorithm driven cloud. Vehicles may exchange data in real time make decisions help infrastructure cloud via seamless made possible networks' high-speed, low-latency connection. algorithm, hosted on servers, can use this to forecast several metrics, including traffic flow, energy usage, best way charge. Cloud computing allows more sophisticated analysis prediction models moving processing load from individual cars large, powerful servers. It improves efficiency, decreases congestion, minimizes optimizes charging simulation real-world experiments.

Language: Английский

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An LSTM-Based Network Slicing Classification Future Predictive Framework for Optimized Resource Allocation in C-V2X

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 129300 - 129310

Published: Jan. 1, 2023

With the advent of 5G communication networks, many novel areas research have emerged and spectrum communicating objects has been diversified. Network Function Virtualization (NFV), Software Defined Networking (SDN), are two broader that tremendously being explored to optimize network performance parameters. Cellular Vehicle-to-Everything (C-V2X) is one such example where end-to-end developed with aid intervening slices. Adoption these technologies enables a shift towards Ultra-Reliable Low-Latency Communication (URLLC) across various domains including autonomous vehicles demand hundred percent Quality Service (QoS) extremely low latency rates. Due limitation resources ensure requirements, telecom operators profoundly researching software solutions for resource allocation optimally. The concept Slicing (NS) from connecting devices routed toward suitable meet their requirements. Nevertheless, bias, in terms finding best slice, observed slices renders non-optimal distribution resources. To cater issues, Deep Learning approach this paper. incoming traffic allocated based on data-driven decisions as well predictive analysis future. A Long Short Term Memory (LSTM) time series prediction adopted optimal utilization, lower rates, high reliability network. model will further packet prioritization retain margin crucial ones.

Language: Английский

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Joint Coding of eMBB and URLLC in Vehicle- to-Everything (V2X) Communications

GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 6

Published: Dec. 4, 2023

A point-to-point communication is considered where a roadside unite (RSU) wishes to simultaneously send messages of enhanced mobile broadband (eMBB) and ultra-reliable low-latency (URLLC) services vehicle. The eMBB message arrives at the beginning block its transmission lasts over entire block. During each block, random arrivals URLLC are assumed. To improve reliability transmissions, RSU reinforces their transmissions by mitigating interference means dirty paper coding (DPC). In proposed scheme, decoded based on two approaches: treating as noise, successive cancellation. Rigorous bounds derived for error probabilities achieved our scheme. Numerical results illustrate that they lower than standard time-sharing.

Language: Английский

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Large-Scale Cellular Vehicle-to-Everything Deployments Based on 5G—Critical Challenges, Solutions, and Vision towards 6G: A Survey

Sensors, Journal Year: 2023, Volume and Issue: 23(16), P. 7031 - 7031

Published: Aug. 8, 2023

The proliferation of fifth-generation (5G) networks has opened up new opportunities for the deployment cellular vehicle-to-everything (C-V2X) systems. However, large-scale implementation 5G-based C-V2X poses critical challenges requiring thorough investigation and resolution successful deployment. This paper aims to identify analyze key associated with In addition, we address obstacles possible contradictions in standards caused by special requirements. Moreover, have introduced some quite influential projects, which influenced widespread adoption technology recent years. As primary goal, this survey provide valuable insights summarize current state field researchers, industry professionals, policymakers involved advancement C-V2X. Furthermore, presents relevant standardization aspects visions advanced 5G 6G approaches upcoming issues mid-term timelines.

Language: Английский

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Federated Learning for Secure and Efficient Vehicular Communications in Open RAN

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: May 14, 2024

This paper presents a comprehensive exploration of federated learning applied to vehicular communications within the context Open RAN. Through an in-depth review existing literature and analysis fundamental concepts, critical challenges are identified current methodologies employed in this sphere. A novel framework is proposed address these shortcomings, fundamentally based on principles. aims enhance security efficiency communications, leveraging flexibility RAN architecture. The further delves into rigorous justification solution, highlighting its potential impact improvements it could bring communications. Ultimately, study provides roadmap for future research applying more secure efficient RAN, opening up new avenues exciting interdisciplinary domain.

Language: Английский

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Network Slicing solutions for Internet of Vehicles (IoV) Networks: A Review

Published: April 24, 2024

The implementation of network slicing services, designed to fulfill the diverse requirements each Vehicle-to-Everything (V2X) use case, has significantly facilitated expansion Internet Vehicles (IoV). Three critical communication needs for V2X ecosystem are ensured by slicing: ultra-low latency and high reliability, enhanced mobile broadband, massive machine-type (mMTC) support. In addition, scalability, mobility, large density taken into account. It is noteworthy that utilization technology evolved beyond its fundamental objectives. this context, application cutting extends objectives encompass aspects essential requirements. This review aims scrutinize categorize scientific research endeavors within domain, according specific underlying adoption in IoV ecosystem.

Language: Английский

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DQN-based Network Selection and Load Balancing for LEO Satellite-Terrestrial Integrated Networks

Published: April 12, 2024

The future communication system will involve collaboration between satellites and terrestrial stations. UEs that directly communicate with low Earth orbit (LEO) experience frequent handovers, which necessitate effective mobility management for user terminals within the LEO satellite-terrestrial integrated network. This paper proposes a deep Q-learning network (DQN) controller based on Markov decision process (MDP) to address this issue. can be utilized adaptive resource allocation selection in networks optimize utilization achieve load balancing.

Language: Английский

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