Results 211 to 220 of about 251,819 (258)

Explainable AI with EDA for V2I path loss prediction. [PDF]

Sci Rep
Ameur MB, Chebil J, Habaebi MH, Tahar JBH, Islam MR, Sheikh AM.   +5 more
europepmc   +1 more source

Reliability-Guided Adaptive Feature Fusion Network for Noise-Robust Bearing Fault Diagnosis. [PDF]

Sensors (Basel)
Yang S, Liu M, Chen Y, Zhang J, Wang P, Luo P.   +5 more
europepmc   +1 more source

Provoking or backfiring? A contingent model of how abusive supervision influences learning from failure through fear. [PDF]

Front Psychol
Wang H, An G, Xu J, Ding L, Chen J.
europepmc   +1 more source

Outage performance of UAV-NOMA networks over rician faded channel with hardware impairments, channel estimation error, and SIC imperfection. [PDF]

PLoS One
Thaherbasha S, Parveen SDN, Medasani S, Turpati S, Addepalli T, Sharma M, Pathan S.   +6 more
europepmc   +1 more source

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Multiplicative based path loss model

International Journal of Communication Systems, 2018
SummaryWe present a newly introduced multiplicative based path loss model for the wireless channel. The model verified with experimental data at 2100 MHz collected across Cyprus in 6 existing microcells in urban, suburban, and rural areas. The new method uses the multiplicative least square fitting model that relates the decibel path loss to the ...
Bülent Bilgehan, Stephen Ojo
openaire   +3 more sources

Path loss model for crowd counting

2017 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2017
Crowd counting using wireless sensing relies on either participatory or non-participatory methods. In both cases, the algorithm developed would predict the number of people or their density within the area monitored, for the purpose of crowd safety and management. An alternative to such approaches is the path loss modeling. A model is proposed to count
Solahuddin Yusuf Fadhlullah, Widad Ismail   +1 more
openaire   +1 more source

Environment Features-Based Model for Path Loss Prediction

IEEE Wireless Communications Letters, 2022
Yutong Sun, Jianhua Zhang 0001, Yuxiang Zhang 0002, Li Yu, Zhiqiang Yuan, Guangyi Liu 0001, Qixing Wang   +6 more
openaire   +3 more sources

Comparison of Empirical Path Loss Propagation Models with Building Penetration Path Loss Model

International Journal on Communications Antenna and Propagation (IRECAP), 2016
Path loss Propagation models plays a fundamental role in planning and designing of mobile radio communication link. In this paper a building penetration path loss model was developed using AUTOCAD. The model involved the combination of three mechanisms of signal propagation; refraction, reflection and diffraction.
Promise Elechi, Paul Osaretin Otasowie
openaire   +1 more source

Compensation of survivorship bias in path loss modeling

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017
In a received signal strength indicator (RSSI)-based path loss estimation where mobile terminals report the decoding results from the received packets, the reported results are strongly affected by the receiver noise. Because low power instantaneous signals cannot be obtained due to the decoding failure, a path loss estimation considering no effects of
Koya Sato, Kei Inage, Takeo Fujii
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Path Loss Measurement and Modeling for Industrial Environment

2019 IEEE 20th International Conference on High Performance Switching and Routing (HPSR), 2019
As industrial production gradually becomes more intelligent, 5G technology has great application prospects in industrial environments. However, the channel in the industrial environment is different from the channel in the other scenarios, and hence need to be characterized specifically.
Ke Zhang, Bin Li 0017, Xiao Tang 0001, Dawei Wang 0001, Linyuan Wei   +4 more
openaire   +1 more source