Atmospheric Influence on the Path Loss at High Frequencies for Deployment of 5G Cellular Communication Networks

Over the past few decades, the development of cellular communication technology has spanned several generations in order to add sophisticated features in the updated versions. Moreover, different high-frequency bands are considered for advanced cellular generations. The presence of updated generations like 4G and 5G is driven by the rising demand for a greater data rate and a better experience for end users. However, because 5G-NR operates at a high frequency and has significant propagation, atmospheric fluctuations like temperature, humidity, and rain rate might result in poorer signal reception, and higher path loss effects unlike the prior generation, which employed frequencies below 6 GHz. This paper makes an attempt to provide a comparative analysis about the influence of different relative atmospheric conditions on 5G cellular communication for various operating frequencies in any urban microcell (UMi) environment maintaining the real outdoor propagation conditions. In addition, the simulation dataset based on environmental factors has been validated by the prediction of path loss using multiple regression techniques. Consequently, this study also aims to address the performance analysis of regression techniques for stable estimations of path loss at high frequencies for different atmospheric conditions for 5G mobile generations due to various possible radio link quality issues and fluctuations in different seasons in South Asia. Furthermore, in comparison to contemporary studies, the Machine Learning models have outperformed in predicting the path loss for the four seasons in South Asian regions.Comment: Accepted for presentation at THE 14th INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT

AI/ML Techniques for 5G Coverage Drop Prediction

This project introduces Machine Learning techniques to the wireless communication environment. In particular, the objective is to predict when the 5G network will not give coverage to a certain User Equipment. A sort of different techniques could be used to solve this, but the solution provided in this document uses a special type of Artificial Neural Network: The Echo State Network. As will be seen through the document, the project will focus on predicting the 5G Quality Parameter, and by introducing the Artificial Neural Networks it is expected for the solution to be able to predict not just the immediate next value, but the next five to ten values. On the results section a comparison between how the final model predicts the 5G Quality Parameter on different time instances will be provided. Finally, on the conclusions, the objective accomplishment will be discussed.Este proyecto tiene como objetivo introducir técnicas de Machine Learning al entorno de las comunicaciones inalámbricas. En particular, se quieren predecir esos momentos en los que la red 5G es incapaz de dar cobertura a un cierto usuario. Muchas técnicas, tanto dentro como fuera del Machine Learning, se podrían haber usado, pero la solución propuesta en este documento se basa en un tipo concreto de Red Neuronal Artificial: las Echo State Network. Como se podrá observar a lo largo del documento, el objetivo será predecir el nivel de calidad futuro de la red 5G. Haciendo que el modelo esté basado en una Red Neuronal Artificial, se espera que pueda ser capaz de predecir no sólo el valor posterior al recibido, sino también de los cinco a los diez valores posteriores. En la sección de Resultados, diversas comparativas entre la predicción del modelo y el valor real se harán, y finalmente, en la sección de Conclusiones se hará una breve discusión sobre el complimiento de los diferentes objetivos del proyecto.Aquest projecte té com a objectiu introduir tècniques de Machine Learning en l’entorn de les comunicacions inalàmbriques. En concret, es vol predir quan la xarxa de 5G no serà capaç de donar cobertura a un cert usuari. Moltes tècniques, tant dins com fora del Machine Learning, es podrien haver utilitzat, però la solució donada en aquest document s’ha fet a partir d’un tipus de Xarxa Neuronal Artificial: les Echo State Network. Com es podrà veure a mesura que avancem pel document, l’objectiu serà predir el nivell de qualitat donat per la xarxa 5G. Introduint les xarxes neuronals artificials, el que es vol és que el model final sigui capaç de predir, no només l’instant posterior al valor rebut, sinó els cinc o inclús els deu futurs valors. A la secció de Resultats veurem diverses comparatives entre la predicció donada pel model i els valors reals. Finalment, a les conclusions es durà a terme una breu discussió sobre l’acompliment dels objectiu

Novel improvements of empirical wireless channel models and proposals of machine-learning-based path loss prediction models for future communication networks.

Doctoral Degree. University of KwaZulu-Natal, Durban.Path loss is the primary factor that determines the overall coverage of networks. Therefore, 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 massively available bandwidths that will meet projected users’ demands, 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 dissertation proposes new path loss and LOS probability models and efficiently improves 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 in fitting real measured data collected from measurement campaigns. In addition, this research 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 proposed 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 was presented as another proof of the superiority of the proposed improvement. Moreover, this research 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 compared to the existing standard models. After that, the dissertation presents investigations on high-ordering the dependency of the standard CI path loss model on the distance between the transmitting and the receiving antennas at the logarithmic scale. Two improved models are provided and discussed: second-order CI and third-order CI models. The main results reveal that the proposed two models outperform the standard CI model and notable reductions in the shadow fading’s standard deviation values as the model’s order increases, which means that more precision is provided. This part of the dissertation also provides a trade-off study between the model’s accuracy and simplicity