Doppler effect on V2I path loss and V2V channel models

Noh, Sun-Kuk; Kim, Phil-jung; Yoon, Junghyun

doi:10.1109/ictc.2016.7763324

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…Figure 6. 14 The points in blue are used for training and the samples in red are used for testing. Figure 6.15 RMSE curves for the PL during training when using the pre-trained ResNet18 model and training the ResNet18 from scratch.…”

Section: List Of Figuresmentioning

confidence: 99%

“…Additionally, vehicular communication is a key technology for intelligent transportation systems (ITS) due to the need for safer, more efficient, and sustainable transportation [10]. ITS applications consider the exchange of data in different vehicular communication among vehicles and with road infrastructure referred to as vehicle-to-vehicle (V2V) [11,12], vehicle-to-infrastructure (V2I) [13,14,15], and vehicle-to-everything (V2X) [16] to provide secure and reliable wireless communications between vehicles to road infrastructure, and among vehicles, respectively, expected to operate in the frequency band below 6 GHz [17,18], and in millimeter wave [11], oriented to short-range communications [19].…”

Section: Motivationmentioning

confidence: 99%

“…In vehicular communication, the V2I and V2V channels differ and deviate significantly from those in cellular communications [10]. For the V2I, both the transmitter and receiver have low antenna heights, and additionally, the receiver or the transmitter can be moving [14,17]. For the V2V case, the channel is more dynamic due to the relative movement between the vehicles' transmitter and receiver, rapidly changing the surrounding environment [18,27].…”

Section: Overview Of Path Loss Modelingmentioning

confidence: 99%

“…For macrocell coverage, several empirical models have been proposed, such as Egli model [37], Okumura-Hata [38] and Lee model [39]. For the characterization of the vehicular channel, empirical models such as ITU-R P.1411-5, two-ray, log-distance, and street canyon have been proposed [10,14,15,17,19].…”

Section: Overview Of Path Loss Modelingmentioning

confidence: 99%

“…However, the PL prediction for vehicular wireless channels presents a more challenging scenario than the traditional cellular channel [10]. In V2I, both the transmitter and receiver have low antenna heights, and additionally, the receiver or transmission can be in motion [14,17]. In the V2V case, the channel is even more dynamic due to the relative movement between the transmitter and receiver vehicles, rapidly changing the surrounding environment [18,27].…”

Section: Path Loss Prediction For V2i and V2v Using Machine Learning ...mentioning

confidence: 99%

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On Machine Learning Techniques Toward Path Loss Modeling in 5g and Beyond Wireless Systems

ELEDUVITH NUNEZ RUIZ

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I would like to express my sincere gratitude to my co-advisor, Professor Lisandro Lovisolo, for his insightful suggestions, enthusiasm, and always being there when I needed his support. This work would not have been possible without his guidance.I would like to acknowledge and thank my family, my parents, my brother, and his wife. They always encouraged me and believed in me.I would like to thank my colleagues at PUC-Rio and CETUC, especially Johnes Ricardo, for their sincere friendship and assistance. A special appreciation to Prof. Guilherme Temporão, Marcelo Balisteri, and Marta Pudwell for their valuable support in the final stage of this journey.I would like to thank my colleagues from the Radio-Propagation Laboratory, CETUC, Carlos Orihuela, Marcelo Molina, Carlos Rodriguez, and Professor Glaucio Lopes, for helping me process the datasets from the measurement campaigns used in this research and always being there when I needed their support.A special appreciation and dedication to my husband Jorge for all the encouragement, love, patience, source of inspiration, and technical support he gave me during this journey. For always looking after my well-being.

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Section: List Of Figuresmentioning

confidence: 99%