The Effects of Rain on Terrestrial Links at K, Ka and E-Bands in South Korea: Based on Supervised Learning

Diba, Feyisa Debo; Samad, Abdus; Choi, Dong-You

doi:10.1109/access.2021.3049825

Cited by 14 publications

(16 citation statements)

References 35 publications

(47 reference statements)

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“…, for R > 10 mm/h , L > 5 km. (12) This model improves the limitation of the 5-min rain rate requirement of the original Lin's model. This model's drawback is that it was only tested at 11 GHz and not at higher frequencies.…”

Section: Garcia Modelmentioning

confidence: 94%

“…In recent technological developments, artificial intelligence (AI) has played an essential role in finding optimized and practical solutions to techniques. Many AI sub-domain techniques and applications have been reported in [12,95,96]. The field of AI has been vast and has existed for many decades.…”

Section: Use Of Learning Techniquesmentioning

confidence: 99%

“…In some studies, attenuation because of rain was reported at even lower frequencies, such as 5 GHz [3] and 7 GHz [4]. Many rain attenuation models have been proposed in the literature, and researchers have attempted to improve existing models to fit with local climatic conditions [5][6][7][8][9][10][11][12]. The accurate estimation of attenuation because of rain in a specific radio link is essential for planning the link budget, maintaining the link quality, and designing the system.…”

Section: Introductionmentioning

confidence: 99%

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A Survey of Rain Attenuation Prediction Models for Terrestrial Links—Current Research Challenges and State-of-the-Art

Samad

Diba

Choi

2021

Sensors

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Millimeter-wave (30–300 GHz) frequency is a promising candidate for 5G and beyond wireless networks, but atmospheric elements limit radio links at this frequency band. Rainfall is the significant atmospheric element that causes attenuation in the propagated wave, which needs to estimate for the proper operation of fade mitigation technique (FMT). Many models have been proposed in the literature to estimate rain attenuation. Various models have a distinct set of input parameters along with separate estimation mechanisms. This survey has garnered multiple techniques that can generate input dataset for the rain attenuation models. This study extensively investigates the existing terrestrial rain attenuation models. There is no survey of terrestrial rain mitigation models to the best of our knowledge. In this article, the requirements of this survey are first discussed, with various dataset developing techniques. The terrestrial links models are classified, and subsequently, qualitative and quantitative analyses among these terrestrial rain attenuation models are tabulated. Also, a set of error performance evaluation techniques is introduced. Moreover, there is a discussion of open research problems and challenges, especially the exigency for developing a rain attenuation model for the short-ranged link in the E-band for 5G and beyond networks.

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Section: Garcia Modelmentioning

confidence: 94%

Section: Use Of Learning Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Survey of Rain Attenuation Prediction Models for Terrestrial Links—Current Research Challenges and State-of-the-Art

Samad

Diba

Choi

2021

Sensors

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“…The received signal power and rain rate data were simultaneously collected at the receivers over four years over the above links. The detailed link profile and the experimental setups are given in [43], [44].…”

Section: A Terrestrial Links Measurement Campaign In South Koreamentioning

confidence: 99%

Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results

et al. 2021

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Recently, wireless telecommunication networks have become a promising alternative for rainfall measuring instruments that complement existing monitoring devices. Due to big dataset of rainfall and telecommunication networks data, empirical computational methods are less adequate representation of the actual data. Therefore, deep learning models are proposed for the analysis of big data and give more accurate representation of real measurements. In this study, we investigated rainfall monitoring results from experimental measurements and deep learning approaches such as artificial neural networks and long short-term memory. The experimental setups were in South Korea over terrestrial and satellite links, and in Ethiopia over terrestrial link for different frequency bands and link distances. The received signal level and rainfall data measurement covered four years in South Korea and the data were sampled at intervals of 10 seconds. In Ethiopia, the data were recorded over 10 months and sampled at intervals of 15 minutes. The received signal power data were used to derive the rainfall rate distribution and compared to actual rainfall measurements over the same time periods. Our results demonstrate that the proposed deep learning-based models generally have a good fit with the measured rainfall rates. The rainfall rate generated from terrestrial links was a better fit to the actual rainfall rate data than that generated from satellite links.

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“…Furthermore, studies from [14], [15] show that the path reduction statistical prediction models provide good prediction accuracy with lower mmWave bands such as 26 GHz and 38 GHz for path lengths longer than 1 kilometer (km). However, for path lengths not exceeding 1 km, the performance of most models has proven inaccurate [16], [17], [18], [19]. It can be concluded that the path reduction factor value during precipitation events is the essential yardstick to evaluate the performance of mmWave communication links.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Some Path Reduction Factor Models Performance i n Tropical Location

Mom¹,

Tyokighir²,

Igwue³

2021

IJRTE

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Performance evaluation of the ITU-R. P.530-17, Ghiani and Budalal model are considered for this work. It is found that the predicted values from the ITU-R and Ghiani distance factor models are seen to gradually decrease with an increase in path length for distances below 1km. Results further suggest that for a link length of 300 m, the Ghiani model predicts a 0.2499 dB (1.059 w) to 0.3273 dB (1.078 w) precipitation loss across all four (4) stations. For the ITU-R. P.530-17 model, a 3.4741 dB (2.225 w) to 5.329 dB (3.411 w) precipitation loss is estimated across all stations while the Budalal model estimated a 2.8608 dB (1.932 w) to 4.6250 dB (2.901 w) precipitation loss across all stations. The ITU-R. P.530-17, Ghiani and Budalal model further suggest a precipitation loss in the Received Signal Strength (RSS) of a typical 5G base station operating in the four (4) stations considered to be at least -9.4733 dBm, -8.8601 dBm, and -6.2489 dBm respectively. Generally, all models are found to predict rain attenuation and distance factor values with disparities especially for link lengths above 300 m. Further research is recommended on the models for accurate prediction and improve agreement with measured values.

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The Effects of Rain on Terrestrial Links at K, Ka and E-Bands in South Korea: Based on Supervised Learning

Cited by 14 publications

References 35 publications

A Survey of Rain Attenuation Prediction Models for Terrestrial Links—Current Research Challenges and State-of-the-Art

A Survey of Rain Attenuation Prediction Models for Terrestrial Links—Current Research Challenges and State-of-the-Art

Wireless Telecommunication Links for Rainfall Monitoring: Deep Learning Approach and Experimental Results

Evaluation of Some Path Reduction Factor Models Performance i n Tropical Location

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