Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G

Chen, Shanzhi; Hu, Jinling; Shi, Yan; Peng, Ying; Fang, Junhua; Zhao, Rui; Zhao, Li

doi:10.1109/mcomstd.2017.1700015

Cited by 778 publications

(333 citation statements)

References 4 publications

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“…This is done to verify any observed trends. Table I summarizes the simulation parameters describing the physical environment for both scenarios in terms of the number of vehicles, number of nodes, and the [7][8][9][10][11][12][13][14][15] computational resources available at these nodes [54]- [56]. The results are averaged over 100 runs for each value.…”

Section: A Simulation Setupmentioning

confidence: 99%

Edge-Enabled V2X Service Placement for Intelligent Transportation Systems

Moubayed

Shami

Heidari

et al. 2021

IEEE Trans. on Mobile Comput.

143

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Vehicle-to-everything (V2X) communication and services have been garnering significant interest from different stakeholders as part of future intelligent transportation systems (ITSs). This is due to the many benefits they offer. However, many of these services have stringent performance requirements, particularly in terms of the delay/latency. Multi-access/mobile edge computing (MEC) has been proposed as a potential solution for such services by bringing them closer to vehicles. Yet, this introduces a new set of challenges such as where to place these V2X services, especially given the limit computation resources available at edge nodes. To that end, this work formulates the problem of optimal V2X service placement (OVSP) in a hybrid core/edge environment as a binary integer linear programming problem. To the best of our knowledge, no previous work considered the V2X service placement problem while taking into consideration the computational resource availability at the nodes. Moreover, a low-complexity greedy-based heuristic algorithm named "Greedy V2X Service Placement Algorithm" (G-VSPA) was developed to solve this problem. Simulation results show that the OVSP model successfully guarantees and maintains the QoS requirements of all the different V2X services. Additionally, it is observed that the proposed G-VSPA algorithm achieves close to optimal performance while having lower complexity. 1) Communication Modes: To cover all possible on-road interactions, the 3GPP project proposed four different communication modes. This includes vehicle-to-network (V2N), vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and vehicle-to-pedestrian (V2P) communication [5] as shown in Fig. 2. Depending on the service or application, a communication mode can be chosen. A brief overview of each of these communication modes is provided below: a-V2N Communication: V2N communication refers to the communication between a vehicle and a V2X application server. This is typically done using a cellular network such as an LTE network [9], [10]. Through this connection, different services such as infotainment, traffic optimization, navigation, and safety can be offered [11], [12].

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Section: A Simulation Setupmentioning

confidence: 99%

Edge-Enabled V2X Service Placement for Intelligent Transportation Systems

Moubayed

Shami

Heidari

et al. 2021

IEEE Trans. on Mobile Comput.

143

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“…Note that the full dimension U2X communications in the cellular Internet of UAVs is different from the existing works on the cellular vehicular to everything (V2X) for the following three reasons. First, the U2X communications are mostly utilized for data sensing and transmission, while the V2X communications are designed for improving road safety and traffic efficiency in priority [18]. Therefore, the optimization target of U2X and V2X communications are different.…”

Section: Introductionmentioning

confidence: 99%

Beyond D2D: Full Dimension UAV-to-Everything Communications in 6G

Zhang

Song

2020

IEEE Trans. Veh. Technol.

120

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In this paper, we consider an Internet of unmanned aerial vehicles (UAVs) over cellular networks, where UAVs work as aerial users to collect various sensory data, and send the collected data to their transmission destinations over cellular links. Unlike the terrestrial users in the conventional cellular networks, different UAVs have various communication requirements due to their sensing applications, and a more flexible communication framework is in demand. To tackle this problem, we propose a UAV-to-Everything (U2X) networking, which enables the UAVs to adjust their communication modes full dimensionally according to the requirements of their sensing applications. In this article, we first introduce the concept of U2X communications, and elaborate on its three communication modes. Afterwards, we discuss the key techniques of the U2X communications, including joint sensing and transmission protocol, UAV trajectory design, and radio resource management. A reinforcement learningbased mathematical framework for U2X communications is then proposed. Finally, the extensions of the U2X communications are presented.2) UAV Transmission: After UAV sensing, the collected data will be transmitted to the corresponding BS or UE, which is defined as the transmission destination. For each UAV, the transmission channel model and frequency band are regulated by the 3GPP [23]. To satisfy the transmission requirements raised by various applications, we provide three communication modes for the UAVs, namely U2N communications, U2U communications, and U2D communications. The communication mode for each link is selected by the UAV according to its location and the communication requirements of the sensing application. The descriptions of these three communication modes will be elaborated on in Section II-B.

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“…The anticipated development of autonomous driving and connected vehicles will bring numerous vehicle-to-everything (V2X) applications, including both safetyservice and entertainment related services, which lead to a wide spectrum of Quality of Service (QoS) requirements in vehicular networks [1]. Provisioning of V2X services require diverse data rate, reliability and latency from vehicular networks [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Network Slicing for V2X Services Toward 5G

2019

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Benefiting from the widely deployed LTE infrastructures, the fifth generation (5G) wireless networks has been becoming a critical enabler for the emerging vehicle-to-everything (V2X) communications.However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and ML algorithm design, are discussed to tackle the related challenges.Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With the integration of 5G network slicing and ML-enabled technologies, the QoS of V2X services is expected to be dramatically enhanced. Index TermsV2X services, network slicing, artificial intelligence, deep reinforcement learning Jie Mei is with the Intelligent Computing and Communication (IC 2 ) Lab, Wireless Signal Processing and Network

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Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G

Cited by 778 publications

References 4 publications

Edge-Enabled V2X Service Placement for Intelligent Transportation Systems

Edge-Enabled V2X Service Placement for Intelligent Transportation Systems

Beyond D2D: Full Dimension UAV-to-Everything Communications in 6G

Intelligent Network Slicing for V2X Services Toward 5G

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