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Unlimited access to information and data sharing wherever and at any time for anyone and anything is a fundamental component of fifth-generation (5G) wireless communication and beyond. Therefore, it has become inevitable to exploit the super-high frequency (SHF) and millimeter-wave (mmWave) frequency bands for future wireless networks due to their attractive ability to provide extremely high data rates because of the availability of vast amounts of bandwidth. However, due to the characteristics and sensitivity of wireless signals to the propagation effects in these frequency bands, more accurate path loss prediction models are vital for the planning, evaluating, and optimizing future wireless communication networks. This paper presents and evaluates the performance of several well-known machine learning methods, including multiple linear regression (MLR), polynomial regression (PR), support vector regression (SVR), as well as the methods using decision trees (DT), random forests (RF), K-nearest neighbors (KNN), artificial neural networks (ANN), and artificial recurrent neural networks (RNN). RNNs are mainly based on long short-term memory (LSTM). The models are compared based on measurement data to provide the best fitting machine-learning-based path loss prediction models. The main results obtained from this study show that the best root-mean-square error (RMSE) performance is given by the ANN and RNN-LSTM methods, while the worst is for the MLR method. All the RMSE values for the given learning techniques are in the range of 0.0216 to 2.9008 dB. Furthermore, this work shows that the models (except for the MLR model) perform excellently in fitting actual measurement data for wireless communications in enclosed indoor environments since they provide R-squared and correlation values higher than 0.91 and 0.96, respectively. The paper shows that these learning methods could be used as accurate and stable models for predicting path loss in the mmWave frequency regime.

Path loss is the primary aspect that determines the overall coverage of networks. Designing reliable wireless communication systems requires accurate path loss prediction models. Future wireless mobile systems will rely mainly on the super-high frequency (SHF) and the millimeter-wave (mmWave) frequency bands due to the massive available bandwidths that will meet projected users' demand, such as the needs of the fifth-generation (5G) wireless systems and other high-speed multimedia services. However, these bands are more sensitive and exhibit a different propagation behavior compared to the frequency bands below 6 GHz. Hence, improving the existing models and developing new models are vital for characterizing the wireless communication channel in both indoor and outdoor environments for future SHF and mmWave services. This paper proposes an efficient improvement of the well-known close-in (CI) free space reference distance model and the floating-intercept (FI) model. Real measured data was taken for both line-of-sight (LOS) and non-line-of-sight (NLOS) communication scenarios in a typical indoor corridor environment at three selected frequencies within the SHF band, namely 14 GHz, 18 GHz, and 22 GHz. The research finding of this work reveals that the proposed models have better performance in terms of their accuracy of fitting real measured data collected from measurement campaigns. In addition, this work studies the impact of the angle of arrival and the antenna heights on the current and improved CI and FI models. The results show that the improved models provide better stability and sensitivity to the change of these parameters. Furthermore, the mean square error between the models and their improved versions were presented. Finally, this paper shows that shadow fading's standard deviation can have a notable reduction in both the LOS and NLOS scenarios (especially in the NLOS), which means higher precision in predicting the path loss.INDEX TERMS Path loss, propagation measurements, 5G, angle of arrival, antenna height.

Abstract-A study of the raindrop size distribution along the eastern coast of South Africa (Durban) is presented. The Biweight kernel estimator based on distometer measurement is used to determine the best estimate of the measured raindrop size probability distribution function (pdf). The best kernel estimator, which results in the lowest integral square error (ISE), is used to measure the closeness of the estimated lognormal and gamma pdf of raindrop size to the measured raindrop size distribution. It is established that the optimised lognormal pdf slightly outperforms the optimised gamma pdf in terms of the mean ISE and the RMSE values, with mean ISE values of 0.026 for lognormal and 0.04 for gamma distributions, respectively, and corresponding mean RMSE values of 0.073 and 0.081, respectively. The method-of-moments gamma and lognormal distributions are observed to be worse estimators of the measured pdf than the two optimized distributions. The N (D) distributions using the optimised lognormal and gamma distributions for the region are compared with those for different tropical regions, namely, India, Singapore, Nigeria, Indonesia, and Brazil. While the Indian lognormal N (D) model gives the highest peak for low raindrop sizes for all rain rates, Durban's gamma and lognormal models exhibit the widest raindrop size spread over all rain rates ranging from 1-120 mm/h. Finally, the specific attenuation due to rain using the Durban models are compared against the ITU-R models and actual measurements over a 19.5 GHz LOS link; the results indicate a need for further work involving both distrometer and radio link measurements for rain rates exceeding 30 mm/h in the eastern coast of South Africa.

Rain fade in radio networks is generated from random fluctuations of rainfall rates, within rain events of spatiotemporal dimensions. These events can be represented as a catenation of single rain spikes occurring as a possible three-stage processbirth, overlap and death. Using the queueing theory approach, the birth-death characteristics of single spikes are investigated as inter-arrival and service time distributions. A total of 548 spike samples from rainfall events in Durban (29°52'S, 30°58'E), South Africa are examined based on distrometer measurements. Rainfall regime analysis of drizzle, widespread, shower and thunderstorm bounds is applied to determine the queue pattern. It is found that the queue patterns in Durban exhibit an Erlang-k distribution (E k ) for both the service and overlap times, while exponential distribution (M) is suitable for inter-arrival time. The mean error statistics for the regimes give root-mean-square errors of 0.64, 1.3 and 2.02% for the service, inter-arrival and overlap distribution, respectively, with acceptable Chi-Squared (χ 2 ) statistics. The M/E k /s/∞ steadystate analysis is later undertaken to investigate the performance of the proposed queue system. Based on the overall data, a power-law relationship is found to exist between the service time and peak rain rate per spike.

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[1] The forward scattering amplitudes for the spherical raindrops are determined for all raindrop sizes at different frequencies by using the Mie scattering theory. The real parts of the extinction cross sections are used to generate power law models at different frequencies. These are integrated over different established raindrop-size distribution models to formulate rain attenuation models. Using the developed rain attenuation models with 5 year rain rate statistics at R 0.01 determined in previous work, the specific rain attenuation is computed. The experimental results obtained from the horizontally polarized signal level measurements recorded in Durban for different rain attenuation bounds are compared with the theoretical results. Finally, the best theoretical model is used to estimate the seasonal cumulative distribution of rain attenuation for Durban, South Africa.Citation: Odedina, M. O., and T. J. Afullo (2010), Determination of rain attenuation from electromagnetic scattering by spherical raindrops: Theory and experiment, Radio Sci., 45, RS1003,

Electromagnetic radiation (EMR) is emitted from electromagnetic fields that surround power lines, household appliances and mobile phones. Research has shown that there are connections between EMR exposure and cancer and also that exposure to EMR may result in structural damage to neurons. In a study by Salford et al. (Environ Health Perspect 111:881-883, 2003) the authors demonstrated the presence of strongly stained areas in the brains of rats that were exposed to mobile phone EMR. These darker neurons were particularly prevalent in the hippocampal area of the brain. The aim of our study was to further investigate the effects of EMR. Since the hippocampus is involved in learning and memory and emotional states, we hypothesised that EMR will have a negative impact on the subject's mood and ability to learn. We subsequently performed behavioural, histological and biochemical tests on exposed and unexposed male and female rats to determine the effects of EMR on learning and memory, emotional states and corticosterone levels. We found no significant differences in the spatial memory test, and morphological assessment of the brain also yielded non-significant differences between the groups. However, in some exposed animals there were decreased locomotor activity, increased grooming and a tendency of increased basal corticosterone levels. These findings suggested that EMR exposure may lead to abnormal brain functioning.