Physical Layer Evaluation of V2X Communications Technologies: 5G NR-V2X, LTE-V2X, IEEE 802.11bd, and IEEE 802.11p

Anwar, Waqar; Franchi, Norman; Fettweis, Gerhard

doi:10.1109/vtcfall.2019.8891313

Cited by 143 publications

(85 citation statements)

References 6 publications

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“…configuring the NR sidelink. The performance boost derived from the use of the previous technological enhancements has begun to be evaluated recently at the PHY level in studies like, for example, [51], [52], which corroborate that NR V2X may satisfy the stringent requirements of V2X use cases. For example, the theoretical analysis in [52] shows that NR V2X may achieve minimum transmission latency in the range 0.25-1 ms, while LTE V2X is unable to achieve a latency below 1 ms.…”

Section: ) Key Ran Features Of Nr-based V2xmentioning

confidence: 88%

“…The performance boost derived from the use of the previous technological enhancements has begun to be evaluated recently at the PHY level in studies like, for example, [51], [52], which corroborate that NR V2X may satisfy the stringent requirements of V2X use cases. For example, the theoretical analysis in [52] shows that NR V2X may achieve minimum transmission latency in the range 0.25-1 ms, while LTE V2X is unable to achieve a latency below 1 ms. The average data rates of 13-30 Mb/s (there are better for large packet sizes, and short distances) for NR V2X are remarkable [52], and their simulations for an urban non lineof-sight scenario confirm that it may be up to 60% higher than LTE V2X; however, it must be noted that the range and reliability of LTE V2X and NR V2X seem to be similar.…”

Section: ) Key Ran Features Of Nr-based V2xmentioning

confidence: 88%

“…For example, the theoretical analysis in [52] shows that NR V2X may achieve minimum transmission latency in the range 0.25-1 ms, while LTE V2X is unable to achieve a latency below 1 ms. The average data rates of 13-30 Mb/s (there are better for large packet sizes, and short distances) for NR V2X are remarkable [52], and their simulations for an urban non lineof-sight scenario confirm that it may be up to 60% higher than LTE V2X; however, it must be noted that the range and reliability of LTE V2X and NR V2X seem to be similar.…”

Section: ) Key Ran Features Of Nr-based V2xmentioning

confidence: 99%

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V2X Support in 3GPP Specifications: From 4G to 5G and Beyond

et al. 2020

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The connected car concept is gaining momentum from the point of view, not only of research, but also of standardization and industrial development. Today there are many options for connecting a vehicle, in terms of to whom and how. In addition to making use of any conventional cellular technology, and connecting the vehicle to a base station or infrastructure element, vehicles can connect wirelessly and directly to each other using different technologies, both from the Institute of Electrical and Electronics Engineers (IEEE) and from the 3rd Generation Partnership Project (3GPP). This article offers a rigorous and detailed review of the system architecture aspects involved in the support of vehicular communications by the 3GPP fifth-generation (5G) standard, with special emphasis on its most recent iteration: Release 16.

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Section: ) Key Ran Features Of Nr-based V2xmentioning

confidence: 88%

Section: ) Key Ran Features Of Nr-based V2xmentioning

confidence: 88%

Section: ) Key Ran Features Of Nr-based V2xmentioning

confidence: 99%

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V2X Support in 3GPP Specifications: From 4G to 5G and Beyond

et al. 2020

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“…Similar to the IEEE 802.11p US standard, both LTE-based V2X and ITS-G5 rely on Orthogonal Frequency Division Multiplexing (OFDM) on the PHY layer, while maintaining the subcarriers orthogonality could be challenging in the vehicular environment leading to a high Bit Error Rate (BER). This is why the 5G cellular network can be a promising technology to support the vehicular communications [5], namely New Radio-V2X (NR-V2X).…”

Section: Introductionmentioning

confidence: 99%

“…Although LDS-OFDM and Joint Sparse Graph-Isotropic Orthogonal Transfer Algorithm (JSG-IOTA) have shown improved performance evaluation, these schemes have only been analysed over a frequency selective channel. Our objective is to extend the state-of-art work to evaluate the performance of LDS over a high mobility channel to simulate the vehicular environment for V2X applications and combine it with other advanced 5G waveforms, specifically for an application that requires a high data rate, a medium number of users connected to the network in a certain geographical area and a time of latency smaller than 1 ms [5].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of LDS Multi Access Technique and New 5G Waveforms for V2X Communication

2020

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Low Density Signature (LDS) is an emerging non-orthogonal multiple access (NOMA) technique that has never been evaluated under a vehicular channel in order to simulate the environment of a vehicle to everything (V2X) communication. Moreover, the LDS structure has been combined with only Orthogonal Frequency Division Multiplexing (OFDM) and Filter-Bank Multi-Carrier (FBMC) waveforms to improve its performances. In this paper, we propose new schemes where the LDS structure is combined with Universal Filtered Multi-Carrier (UFMC) and Filtered-OFDM waveforms and the Bit Error Rate (BER) is analysed over a frequency selective channel as a reference and over a vehicular channel to analyse the effect of the Doppler shift on the overall performance.

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PAM: Predictive analytics and modules‐based computation offloading framework using greedy heuristics and 5G NR‐V2X

Khattak,

Yuan,

Ahmad

et al. 2024

Trans Emerging Tel Tech

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Recent advancements in distributed computing systems have shown promising prospects in enabling the effective usage of many next‐generation applications. These applications include a wide range of fields, such as healthcare, interactive gaming, video streaming, and other related technologies. Among such solutions are the evolving vehicular fog computing (VFC) frameworks that make use of IEEE and 3GPP protocols and use advanced optimization algorithms. However, these approaches often rely on outdated protocols or computationally intensive mathematical techniques for solving or representing their optimization models. Additionally, some of these frameworks have not thoroughly considered the type of application during their evaluation and validation phases. In response to these challenges, we have developed the “predictive analytics and modules” (PAM) framework, which operates on a time and event‐driven basis. It utilizes up‐to‐date 3GPP protocols to address the inherent unpredictability of VFC‐enabled distributed computing systems required in smart healthcare systems. Through a combination of a greedy heuristic approach and a distributed offloading architecture, PAM efficiently optimizes decisions related to task offloading and computation allocation. This is achieved through specialized algorithms that provide support to computationally weaker devices, all within a time frame of under 100 ms. To assess the performance of PAM in comparison to three benchmark methodologies, the evaluation pathways that we employed are average response time, probability density function, pareto‐analysis, algorithmic run time, and algorithmic complexity.

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Physical Layer Evaluation of V2X Communications Technologies: 5G NR-V2X, LTE-V2X, IEEE 802.11bd, and IEEE 802.11p

Cited by 143 publications

References 6 publications

V2X Support in 3GPP Specifications: From 4G to 5G and Beyond

V2X Support in 3GPP Specifications: From 4G to 5G and Beyond

Performance Analysis of LDS Multi Access Technique and New 5G Waveforms for V2X Communication

PAM: Predictive analytics and modules‐based computation offloading framework using greedy heuristics and 5G NR‐V2X

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