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

Sambit Pattanaik, Agbotiname Lucky Imoize (), Chun-Ta Li (), Sharmila Anand John Francis, Cheng-Chi Lee () and Diptendu Sinha Roy
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Sambit Pattanaik: Department of Computer Science & Engineering, National Institute of Technology Meghalaya, Meghalaya 793003, India
Agbotiname Lucky Imoize: Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria
Chun-Ta Li: Bachelor’s Program of Artificial Intelligence and Information Security, Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 24205, Taiwan
Sharmila Anand John Francis: Scientific Research Unit (SRU), Rejal Almaa Campus, King Khalid University, Abha 61421, Saudi Arabia
Cheng-Chi Lee: Department of Library and Information Science, Research and Development Center for Physical Education, Health, and Information Technology, Fu Jen Catholic University, New Taipei City 24205, Taiwan
Diptendu Sinha Roy: Department of Computer Science & Engineering, National Institute of Technology Meghalaya, Meghalaya 793003, India

Mathematics, 2023, vol. 11, issue 13, 1-20

Abstract: 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.

Keywords: millimeter wave; building height estimation; deep convolutional neural network; image segmentation; image processing; camera projection framework (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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