Intelligent super-fast Vehicle-to-Everything 5G communications with predictive switching between mmWave and THz links

Rasheed, Iftikhar; Hu, Fei

doi:10.1016/j.vehcom.2020.100303

Cited by 35 publications

(24 citation statements)

References 33 publications

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“…As identified in previous literature, the advantages of 5G over 4G and LTE includes increased spectrum allocation, improved capacity to aggregate simultaneous users within the coverage area, availability of directional beamforming antennas, highly increased bit rates within increased proportions of the 5G coverage areas, and lower infrastructure cost [3]. Researchers are showing more interest in spectrums higher than 6 GHz to improve communication network reliability and throughput in the transportation CPS [4], [5]. The frequencies under 6 GHz are already allocated to various LTE bands [6].…”

Section: A Backgroundmentioning

confidence: 99%

Performance Evaluation of 5G Millimeter-Wave based Vehicular Communication for Connected Vehicles

Khan¹,

Khan²,

Chowdhury³

et al. 2021

Preprint

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Due to the gradual increase in the volume of data generated by connected vehicles (CV), future vehicle-to-infrastructure (V2I) applications will require a communication medium that offers high-speed (high bandwidth) while maintaining reliability in high-mobility traffic scenarios. The 5G millimeter-wave (mmWave) can solve the communication issues related to V2I applications. However, the performance of the 5G mmWave for vehicular communication in high-mobility urban traffic scenarios is yet to be evaluated. This study presents a case study on assessing the performance of the 5G mmWave based vehicular communication in such traffic scenarios. We have designed three realistic use cases for performance evaluation based on three challenges: increased CV penetration level, dynamic mobility, and V2I application specifications, such as data rate and packet size. Then, we have created a simulation-based experimental setup using a microscopic traffic simulator (SUMO) and a communication network simulator (ns-3) to simulate the use cases. We have used delay, packet loss, throughput, and signal-to-interference-plus-noise ratio (SINR) as the communication performance evaluation metrics. Our analyses found that the CV penetration level is the primary determinant of the performance of the 5G mmWave. Moreover, once the data rate is increased by a factor of 40, delay and packet loss increase by factors of 6.8 and 2.8, respectively.

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Section: A Backgroundmentioning

confidence: 99%

Performance Evaluation of 5G Millimeter-Wave based Vehicular Communication for Connected Vehicles

Khan¹,

Khan²,

Chowdhury³

et al. 2021

Preprint

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“…They proposed a reinforcement learning approach to handle the beam selection problem in a high-mobility vehicular environment. The authors of [46] addressed optimal beam selection in mmWave-based V2X communications by designing the multitype-2 fuzzy inference system.…”

Section: Mmwave Beam Managementmentioning

confidence: 99%

Network Capacity Optimization for Cellular‐Assisted Vehicular Systems by Online Learning‐Based mmWave Beam Selection

Gui

Liu

Deng

et al. 2021

Wireless Communications and Mobile Computing

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Directional communication is helpful to improve the performance of millimeter Wave (mmWave) links. However, the dynamic nature of vehicular scenarios raises the complexity of directional mmWave vehicular communications. Also, a mmWave link is susceptible to blockages. Therefore, a mmWave vehicular communication system requires high environmental adaptability and context-awareness. Due to inadequate context information and insufficient beam settings in the existing related algorithm, it is difficult to pick out the set of beams with more reasonable widths and directions, which hinders the further promotion of network capacity in vehicular networks. Therefore, we propose an improved fast machine learning (IFML) algorithm to overcome this shortcoming. In order to improve network capacity while suppressing the additional beam search overhead, a partitioned search method is designed in the IFML. Also, in order to be robust to occasional fluctuations and timely adapt to significant changes in communication environments, the IFML adopts a flexible beam performance update approach based on adjustable weight coefficient. The simulation results show that the IFML significantly outperforms the existing related algorithm in terms of aggregate received data after a certain number of online learning time periods.

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“…[47,48]. Furthermore, it is important to note that in this paper, we identify users or vehicles in the V2X tier as URLLC users owing to the peculiarities of V2X communications [49,50,51,52]. Moreover, we give a detailed explanation of the V2X layer in Subsection 3.4.…”

Section: System Modelmentioning

confidence: 99%

Multi-tier multi-tenant network slicing: A multi-domain games approach

Oladejo¹,

Ekwe²,

Akinyemi³

2021

ITU-J FET

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The 5G slice networks will play a critical role in meeting the stringent quality-of-service requirements of different use cases, reducing the Capital Expenditure (CapEX) and Operational Expenditure (OpEX) of mobile network operators. Owing to the flexibility and ability of 5G slice networks to meet the needs of different verticals, it attracts new network players and entities to the mobile network ecosystem, and therefore it creates new business models and structures. Motivated by this development, this paper addresses the dynamic resource allocation in a multi-slice multi-tier multi-domain network with different network players. The dynamic resource allocation problem is formulated as a maximum utility optimisation problem from a multiplayer multi-domain perspective. Furthermore, a 3-level hierarchical business model comprising Infrastructure Providers (InPs), Mobile Virtual Network Operators (MVNOs), Service Providers (SPs), and slice users are investigated. We propose two schemes: a multi-tier multi-domain slice user matching game scheme and a distributed backtracking multiplayer multi-domain game scheme in solving the transformed maximum utility optimisation problem. We compare the multi-tier multi-tenant multi-domain game scheme with a Genetic Algorithm (GA) Intelligent Latency-Aware Resource (GI-LARE) allocation scheme, and a static slicing resource allocation scheme via Monte Carlo simulation. Our findings reveal that the proposed scheme significantly outperforms these other schemes.

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Intelligent super-fast Vehicle-to-Everything 5G communications with predictive switching between mmWave and THz links

Cited by 35 publications

References 33 publications

Performance Evaluation of 5G Millimeter-Wave based Vehicular Communication for Connected Vehicles

Performance Evaluation of 5G Millimeter-Wave based Vehicular Communication for Connected Vehicles

Network Capacity Optimization for Cellular‐Assisted Vehicular Systems by Online Learning‐Based mmWave Beam Selection

Multi-tier multi-tenant network slicing: A multi-domain games approach

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