5G for Vehicular Communications

Shah, Syed Adeel Ali; Ahmed, Ejaz; Imran, Muhammad; Zeadally, Sherali

doi:10.1109/mcom.2018.1700467

Cited by 303 publications

(173 citation statements)

References 15 publications

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“…Note that the terms #a 1 -#a 4 are quadratic in which the assumption of queue stability forces them to be bounded. Hence sum of #a 1 -#a 4 terms are replaced by a bounded value ∆ 0 with, ∆ 0 ≥ u∈U (au(t)−ru(t)) 2 2 + (1 qu(t) − ) 2 q 2 u (t)+(au(t)−ru(t)) 2 2 + q 2 u (t)+(au(t)−ru(t)) 2 +M 2 1 2 + (qu(t)−q 0 ) 2 +(au(t)−ru(t)) 2 −M 2 2 2 + (q u (t) − q 0 ) 2 + (a u (t) − r u (t)) 2 B u (t) + 2 (q u (t) − q 0 ) 2 + (q u (t) − q 0 )(a u (t) − r u (t)) (a u (t) − r u (t)) 2 1 qu(t) . (25) Terms #b 1 and #b 2 are independent from the control variables.…”

Section: Appendix a Proof Of Propositionmentioning

confidence: 99%

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Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

Samarakoon

Bennis

Saad

et al. 2020

IEEE Trans. Commun.

326

174

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In this paper, the problem of joint power and resource allocation for ultra reliable low latency communication (URLLC) in vehicular networks is studied. The key goal is to minimize the networkwide power consumption of vehicular users (VUEs) subject to high reliability in terms of probabilistic queuing delays. In particular, using extreme value theory (EVT), a new reliability measure is defined to characterize extreme events pertaining to vehicles' queue lengths exceeding a predefined threshold with non-negligible probability. In order to learn these extreme events in a dynamic vehicular network, a novel distributed approach based on federated learning (FL) is proposed to estimate the tail distribution of the queues. Taking into account the communication delays incurred by FL over wireless links, Lyapunov optimization is used to derive the joint transmit power and resource allocation policies enabling URLLC for each VUE in a distributed manner. The proposed solution is then validated via extensive simulations using a Manhattan mobility model. Simulation results show that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79% reductions in the amount of data that need to be exchanged. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, while reducing the average power consumption by two folds, compared to an average queue-based baseline. For the VUEs with large queue lengths, the proposed method reduces their average queue lengths and fluctuations therein by about 30% compared to the aforementioned baseline. arXiv:1807.08127v2 [cs.IT] 1 Aug 2018 Index Terms V2V communication, Lyapunov optimization, extreme value theory, federated learning, URLLC, 5G. I. INTRODUCTION Providing efficient vehicle-to-vehicle (V2V) communications is a necessary stepping stone for enabling autonomous and intelligent transportation systems (ITS) [1]-[5]. V2V communications can extend drivers' field of view, thus enhancing traffic safety and driving experience, while enabling new transportation features such as platooning, real-time navigation, collision avoidance, and autonomous driving [1], [4]. However, the performance of emerging transportation applications heavily rely on the availability of V2V communication links with extremely low errors and delays. In this regard, achieving ultra-reliable low-latency communication (URLLC) for V2V networks is necessary for realizing the vision of intelligent transportation [1]. Since over-the-air latency and queuing latency are coupled, ensuring low queuing latency is required to achieve the much coveted target end-to-end latency of 1 ms. This, in turn, necessitates efficient radio resource management (RRM) techniques [5]-[7]. Furthermore, the increased energy consumption and its negative impact on the environment due to the large number of vehicles in modern transportation system, and improving energy-efficiency/energy savings need to be addressed ...

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Section: Appendix a Proof Of Propositionmentioning

confidence: 99%

“…(t)+(au(t)−ru(t))2 +M 2 1 + (A u (t) − M 1 )q u (t) − M 1 A u (t) #b 2 + (q u (t) + A u (t) − M 1 )(a u (t) − r u (t)) 1 qu(t) , (qu(t+1)−q 0 ) 2 −M 2 2 B u (t) (q u (t + 1) − q 0 ) 2 + M 2 1 qu(t) , = (qu(t)−q 0 ) 2 +(au(t)−ru(t)) 2 −M 2 2 2…”

mentioning

confidence: 99%

Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

Samarakoon

Bennis

Saad

et al. 2020

IEEE Trans. Commun.

326

174

View full text Add to dashboard Cite

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“…Intelligent transportation: Vehicular communication is an essential part of the modern era that promises to provide ubiquitous connectivity with ultra-reliable and low-latency connectivity [59]. V2X communications improve road safety, traffic efficiency, and availability of infotainment services [60].…”

Section: Connectivity Of Patientmentioning

confidence: 99%

The Role of Optical Wireless Communication Technologies in 5G/6G and IoT Solutions: Prospects, Directions, and Challenges

et al. 2019

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The upcoming fifth- and sixth-generation (5G and 6G, respectively) communication systems are expected to deal with enormous advances compared to the existing fourth-generation communication system. The few important and common issues related to the service quality of 5G and 6G communication systems are high capacity, massive connectivity, low latency, high security, low-energy consumption, high quality of experience, and reliable connectivity. Of course, 6G communication will provide several-fold improved performances compared to the 5G communication regarding these issues. The Internet of Things (IoT) based on the tactile internet will also be an essential part of 5G-and-beyond (5GB) (e.g., 5G and 6G) communication systems. Accordingly, 5GB wireless networks will face numerous challenges in supporting the extensive verities of heterogeneous traffic and in satisfying the mentioned service-quality-related parameters. Optical wireless communication (OWC), along with many other wireless technologies, is a promising candidate for serving the demands of 5GB communication systems. This review paper clearly presents how OWC technologies, such as visible light communication, light fidelity, optical camera communication, and free space optics communication, will be an effective solution for successful deployment of 5G/6G and IoT systems.

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“…Because of the safety concern, European Telecommunications Standard Institute (ETSI) has standardized safety protocols based on two awareness-based messages: decentralized environmental notification message (DENM) and cooperative awareness message (CAM) [40]. To reflect vehicles based on the mentioned safety standards, V2V communication should have the low latency characteristic of URLLC.…”

Section: Urllc and Vehicle-to-vehicle (V2v)mentioning

confidence: 99%

5G Ultra-Reliable Low-Latency Communication Implementation Challenges and Operational Issues with IoT Devices

2019

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To meet the diverse industrial and market demands, the International Telecommunication Union (ITU) has classified the fifth-generation (5G) into ultra-reliable low latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC). Researchers conducted studies to achieve the implementation of the mentioned distributions efficiently, within the available spectrum. This paper aims to highlight the importance of URLLC in accordance with the approaching era of technology and industry requirements. While highlighting a few implementation issues of URLLC, concerns for the Internet of things (IoT) devices that depend on the low latency and reliable communications of URLLC are also addressed. In this paper, the recent progress of 3rd Generation Partnership Project (3GPP) standardization and the implementation of URLLC are included. Finally, the research areas that are open for further investigation in URLLC implementation are highlighted, and efficient implementation of URLLC is discussed.

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5G for Vehicular Communications

Cited by 303 publications

References 15 publications

Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

The Role of Optical Wireless Communication Technologies in 5G/6G and IoT Solutions: Prospects, Directions, and Challenges

5G Ultra-Reliable Low-Latency Communication Implementation Challenges and Operational Issues with IoT Devices

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