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
Rain attenuation becomes significant to degrade the earth-space or terrestrial radio link’s signal-to-noise ratio (SNR). So, to maintain the desired SNR level, with the help of fade mitigation techniques (FMTs), it needs to control transmitted signals power considering the expected rainfall. However, since the rain event is a random phenomenon, the rain attenuation that may be experienced by a specific link is difficult to estimate. Many empirical, physical, and compound nature-based models exist in the literature to predict the expected rain attenuation. Furthermore, many optimizations and decision-making functions have become simpler since the development of the learning-assisted (LA) technique. In this work, the LA rain attenuation (LARA) model was classified based on input parameters. Besides, for comparative analysis, each of the supported frequency components of LARA models were tabulated, and an accurate contribution of each model was identified. In contrast to all the currently available LARA models, the accuracy and correlation of input-output parameters are presented. Additionally, it summarizes and discusses open research issues and challenges.
The indoor application of wave propagation in the 5G network is essential to fulfill the increasing demands of network access in an indoor environment. This study investigated the wave propagation properties of line-of-sight (LOS) links at two long corridors of Chosun University (CU). We chose wave propagation measurements at 3.7 and 28 GHz, since 3.7 GHz is the closest to the roll-out frequency band of 3.5 GHz in South Korea and 28 GHz is next allocated frequency band for Korean telcos. In addition, 28 GHz is the promising millimeter band adopted by the Federal Communications Commission (FCC) for the 5G network. Thus, the 5G network can use 3.7 and 28 GHz frequencies to achieve the spectrum required for its roll-out frequency band. The results observed were applied to simulate the path loss of the LOS links at extended indoor corridor environments. The minimum mean square error (MMSE) approach was used to evaluate the distance and frequency-dependent optimized coefficients of the close-in (CI) model with a frequency-weighted path loss exponent (CIF), floating-intercept (FI), and alpha–beta–gamma (ABG) models. The outcome shows that the large-scale FI and CI models fitted the measured results at 3.7 and 28 GHz.
In present-day society, train tunnels are extensively used as a means of transportation. Therefore, to ensure safety, streamlined train operations, and uninterrupted internet access inside train tunnels, reliable wave propagation modeling is required. We have experimented and measured wave propagation models in a 1674 m long straight train tunnel in South Korea. The measured path loss and the received signal strength were modeled with the Close-In (CI), Floating intercept (FI), CI model with a frequency-weighted path loss exponent (CIF), and alpha-beta-gamma (ABG) models, where the model parameters were determined using minimum mean square error (MMSE) methods. The measured and the CI, FI, CIF, and ABG modelderived path loss was plotted in graphs, and the model closest to the measured path loss was identified through investigation. Based on the measured results, it was observed that every model had a comparatively lower (n < 2) path loss exponent (PLE) inside the tunnel. We also determined the path loss component's possible deviation (shadow factor) through a Gaussian distribution considering zero mean and standard deviation calculations of random error variables. The FI model outperformed all the examined models as it yielded a path loss closer to the measured datasets, as well as a minimum standard deviation of the shadow factor.
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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