Softwarized Resource Management and Allocation With Autonomous Awareness for 6G-Enabled Cooperative Intelligent Transportation Systems

Cao, Haotong; Garg, Sahil; Kaddoum, Georges; Singh, Satinder; Hossain, M. Shamim

doi:10.1109/tits.2022.3209899

Cited by 16 publications

(2 citation statements)

References 29 publications

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“…This habitus is embedded in the mind of the individual, which will determine how he acts, communicates, thinks, and so on. Explain that habitus appears in several forms, such as 1) Empirical tendencies to act, for example, choosing a lifestyle, 2) Motivation or preferences, tastes, and emotions, 3) behavior that becomes a personality, 4) World challenges, 5) practical social skills and abilities, 6) Aspirations related to life changes (Cao et al, 2022).…”

Section: Academic Achievement Culturementioning

confidence: 99%

Human resources for the development of higher education and academic culture of achievement and continuous learning

Endrawati

Sabirin²,

Deluma³

et al. 2023

irjmis

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This paper discussed the development of high-endowment resources and academic culture in learning achievement and sustainability. We get the data online and analyze it under a distinctive approach involving data coding systems, in-depth data interpretation, and drawing conclusions that answer problems with high validity. Our data search is specifically for literature released ten years ago because the development of universities with their vital human resources has experienced extraordinary developments in the last ten years. After a series of analyses, interpretations, and discussions, the results include developing university resources and achieving academic results. A sustainable learning culture will only be possible when the university can optimize existing resources: having a culture of increasing success in obtaining specialization makes high career future opportunities and building relationships that lead to career support after getting knowledge in college. Thus, these results benefit similar studies in the future.

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Section: Academic Achievement Culturementioning

confidence: 99%

Human resources for the development of higher education and academic culture of achievement and continuous learning

Endrawati

Sabirin²,

Deluma³

et al. 2023

irjmis

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show abstract

“…One such future 6G application may be a cyber-physical intelligent transportation system (ITS). Such artificial intelligence-enabled vehicle-to-vehicle communications and vehicle-to-roadside communications use extremely high-frequency bands (EHF) such as mmWave [2]. The vehicles are equipped with advanced sensing technologies such as the Internet of Things (IoT), consumer electronics, intelligent software, and so forth [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Diffraction Loss Estimation for Future Intelligent Transportation Systems in 6G Networks

Pattanaik

Imoize

et al. 2023

Mathematics

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The advancement of 6G networks is driven by the need for customer-centric communication and network control, particularly in applications such as intelligent transport systems. These applications rely on outdoor communication in extremely high-frequency (EHF) bands, including millimeter wave (mmWave) frequencies exceeding 30 GHz. However, EHF signals face challenges such as higher attenuation, diffraction, and reflective losses caused by obstacles in outdoor environments. To overcome these challenges, 6G networks must focus on system designs that enhance propagation characteristics by predicting and mitigating diffraction, reflection, and scattering losses. Strategies such as proper handovers, antenna orientation, and link adaptation techniques based on losses can optimize the propagation environment. Among the network components, aerial networks, including unmanned aerial vehicles (UAVs) and electric vertical take-off and landing aircraft (eVTOL), are particularly susceptible to diffraction losses due to surrounding buildings in urban and suburban areas. Traditional statistical models for estimating the height of tall objects like buildings or trees are insufficient for accurately calculating diffraction losses due to the dynamic nature of user mobility, resulting in increased latency unsuitable for ultra-low latency applications. To address these challenges, this paper proposes a deep learning framework that utilizes easily accessible Google Street View imagery to estimate building heights and predict diffraction losses across various locations. The framework enables real-time decision-making to improve the propagation environment based on users’ locations. The proposed approach achieves high accuracy rates, with an accuracy of 39% for relative error below 2%, 83% for relative error below 4%, and 96% for both relative errors below 7% and 10%. Compared to traditional statistical methods, the proposed deep learning approach offers significant advantages in height prediction accuracy, demonstrating its efficacy in supporting the development of 6G networks. The ability to accurately estimate heights and map diffraction losses before network deployment enables proactive optimization and ensures real-time decision-making, enhancing the overall performance of 6G systems.

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Task placement and resource allocation for UAV and edge computing supported transportation systems

Du,

Zhang,

et al. 2024

J Supercomput

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Softwarized Resource Management and Allocation With Autonomous Awareness for 6G-Enabled Cooperative Intelligent Transportation Systems

Cited by 16 publications

References 29 publications

Human resources for the development of higher education and academic culture of achievement and continuous learning

Human resources for the development of higher education and academic culture of achievement and continuous learning

Data-Driven Diffraction Loss Estimation for Future Intelligent Transportation Systems in 6G Networks

Task placement and resource allocation for UAV and edge computing supported transportation systems

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