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.
Satellite communication is a promising transmission technique to implement 5G and beyond networks. Attenuation due to rain begins at a frequency of 10 GHz in temperate regions. However, some research indicates that such attenuation effects start from 5–7 GHz, especially in tropical regions. Therefore, modeling rain attenuation is significant for propagating electromagnetic waves to achieve the required quality of service. In this survey, different slant link rain attenuation prediction models have been examined, classified, and analyzed, and various features like improvements, drawbacks, and particular aspects of these models have been tabulated. This survey provides various techniques for obtaining input data sets, including rain height, efficient trajectory length measurement techniques, and rainfall rate conversion procedures. No survey of the Earth–space link models for rain attenuation is available to the best of our knowledge. In this study, 23 rain attenuation models have been investigated. For easy readability and conciseness, the details of each model have not been included. The comparative analysis will assist in propagation modeling and planning the link budget of slant links.
At the rise of the fourth industrial revolution, artificial intelligence (AI), along with key enabling technologies such as millimeter waves (mm-waves) can be used to launch the fifth-generation (5G) and beyond communication links. However, the quality of radio links at higher frequency bands is limited by atmospheric elements. Among others, rainfall is the major propagation impairment at millimetric wave bands, which needs to be considered during the link budget planning. In this study, we investigated the rain attenuation results obtained from experimental data, existing models, and proposed supervised artificial neural network (SANN) at K, Ka, and E-bands, respectively, for terrestrial links in South Korea. The measurement campaigns were between Incheon, National Radio Research Agency (RRA) tower station, to the EMS Dongyoksang tower station operating at 75 GHz over a 100-m path length, and between Incheon, RRA tower station to Khumdang, Korea Telecom (KT) tower station, operating at 18 and 38 GHz over a 3.2km path length. The three-year rainfall and received signal level data measurements over these paths were used to determine rain attenuation distributions at different percentages of exceedance time distribution. Additionally, three existing attenuation models, ITU-R 530.17, Lin, and Revised Silva Mello (RSM) models were compared with measured rain attenuation. Our results indicate that these models did not correspond with measured results. Therefore, in this research, we proposed a supervised learning-based attenuation prediction method, which provides better performance than existing models. Furthermore, we validated our proposed model with measured received-signal level and rainfall data at the above-mentioned operating frequencies.INDEX TERMS Artificial neural network, millimeter wave, rain attenuation, South Korea, terrestrial links I. INTRODUCTION
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