5.8 GHz propagation of low‐height wireless links in sea port scenario

Reyes-Guerrero, J. C.; Mariscal, Luis

doi:10.1049/el.2014.0098

Cited by 26 publications

(4 citation statements)

References 7 publications

(6 reference statements)

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“…To study the wireless propagation channel in maritime communication scenarios, the corresponding channel sounders are needed for channel measurement. In [545], a time-domain channel sounder was used to measure the channel characteristics in a sea port scenario. The vertically polarized omnidirectional antennas were equipped at both Tx and Rx sides.…”

Section: A Testbeds For 6g Channelsmentioning

confidence: 99%

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Wang

You

Gao

et al. 2023

IEEE Commun. Surv. Tutorials

395

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Section: A Testbeds For 6g Channelsmentioning

confidence: 99%

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Wang

You

Gao

et al. 2023

IEEE Commun. Surv. Tutorials

395

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“…The direct ray has the maximum electric field since there is a line of sight between transmitter and receiver. The equation of free space is the base of many radio propagation models as the case of log-normal path loss models using for urban environments [18] and seaport environments [26]. The equation of free space is the following:…”

Section: Description Of Geometric Parameters For Ann and Nfsmentioning

confidence: 99%

Radio Propagation Models Based on Machine Learning Using Geometric Parameters for a Mixed City-River Path

et al. 2020

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This work presents and evaluates the use of geometric parameters of the environment in the prediction of the electric field in mixed city-river type environments, employing two techniques of Machine Learning (ML) as Artificial Neural Networks (ANN) and Neuro-Fuzzy System (NFS). For its development, measurements were carried out in Amazon Region, Belém city, in the 521 MHz band. The input parameters for an ANN and NFS are the distance between transmitter and receiver, the distance only over the river, the height of the ground, the radius of the first Fresnel ellipsoid, and the electric field of free space. The ANN is a Multilayer Perceptron Network (MLP) that uses the Levenberg-Marquardt training algorithm and cross-validation method. The NFS is an Adaptive Neuro-Fuzzy Inference System (ANFIS) that uses the model Sugeno. The results obtained compared with the classic literature models (ITU-R 1546 and Okumura-Hata) in the city for distances up 20 km and over the river for distances up 5 km. A quantitative analysis is performed between the measured and predicted data through the Standard deviation (SD), Root Mean Square Error (RMSE), and the Grey Relational Grade, combined with the Mean Absolute Percentage Error (GRG-MAPE). For ANN, the SD is 2.13, the RMSE is 2.11 dB, and the GRG-MAPE is 0.96. Also, for the NFS, the SD is 1.99, the RMSE is 2.06 dB, and the GRG-MAPE is 0.97. It should be noted that the transition zone between the city and the river was characterized by the proposed ANN and NFS in contrast with the classic literature models, which did not demonstrate coherence in the transition zone.INDEX TERMS Artificial neural networks, geometric parameters, neuro-fuzzy systems, mixed city-river path, radio propagation model.

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“…[13], [14], and [16] fit extreme value distribution (EVD) to the whole path-loss or power distribution and then claim that EVD can be used to model fading in wireless channel better than the well-known models. Additionally, in [15] and [17], generalized extreme value (GEV) distribution is used to model the small scale fading at maritime communication, and root-mean-square (RMS) delay spread at V2V communication, respectively. However, EVT has never been incorporated into wireless channel modeling to model the tail statistics nor to use the theorems for the consistency of the distributions, stopping conditions on determining the sufficient number of samples required in EVT, and validation procedures to address reliability constraint at the URLLC levels.…”

Section: Introductionmentioning

confidence: 99%

Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications

Mehrnia¹,

Coleri²

2020

Preprint

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A key building block in the design of ultra-reliable communication systems is a wireless channel model that captures the statistics of rare events and significant fading dips. Ambitious reliability objectives, on the order of 10 5 − 10 9 packet error rate, only make sense when they are related to a statistical model of the environment in which the system is deployed. In this study, we propose a novel methodology based on extreme value theory (EVT) to statistically model the behavior of extreme events in a wireless channel for ultra-reliable communication. This methodology includes techniques for fitting the lower tail of the distribution to the generalized Pareto distribution, determination of optimum threshold over which the tail statistics are derived, derivation of optimum stopping condition on the sufficient number of samples required to apply EVT, and finally an assessment of the validity of the derived model. The analysis demonstrates that the proposed algorithm decreases the number of required samples for modeling the tail statistics by about 7 × 10 5 , and provides the best fit to the collected data and performs much better than the conventional methods based on the extrapolation of the average statistics in the ultra-reliable regime.

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5.8 GHz propagation of low‐height wireless links in sea port scenario

Cited by 26 publications

References 7 publications

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Radio Propagation Models Based on Machine Learning Using Geometric Parameters for a Mixed City-River Path

Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications

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