Literature DB >> 33572178

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

Md Abdus Samad1,2, Feyisa Debo Diba1,3, Dong-You Choi1.   

Abstract

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.

Entities:  

Keywords:  ITU-R model; enhanced synthetic storm technique; millimeter-wave; rain attenuation; rain attenuation time series

Year:  2021        PMID: 33572178      PMCID: PMC7915915          DOI: 10.3390/s21041207

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  2 in total

Review 1.  State-of-the-art in artificial neural network applications: A survey.

Authors:  Oludare Isaac Abiodun; Aman Jantan; Abiodun Esther Omolara; Kemi Victoria Dada; Nachaat AbdElatif Mohamed; Humaira Arshad
Journal:  Heliyon       Date:  2018-11-23

2.  Application of the deep learning for the prediction of rainfall in Southern Taiwan.

Authors:  Meng-Hua Yen; Ding-Wei Liu; Yi-Chia Hsin; Chu-En Lin; Chii-Chang Chen
Journal:  Sci Rep       Date:  2019-09-04       Impact factor: 4.379

  2 in total
  2 in total

Review 1.  A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks.

Authors:  Bin Lian; Zhongcheng Wei; Xiang Sun; Zhihua Li; Jijun Zhao
Journal:  Sensors (Basel)       Date:  2022-06-10       Impact factor: 3.847

2.  Communication Systems Performance at mm and THz as a Function of a Rain Rate Probability Density Function Model.

Authors:  Judy Kupferman; Shlomi Arnon
Journal:  Sensors (Basel)       Date:  2022-08-20       Impact factor: 3.847

  2 in total

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