A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Analysis of Human EMF Exposure in 5G Cellular Systems

Increasing concerns of communications at a frequency spectrum higher than 6 GHz have gained international alarm that suggests more research is needed before it is deployed successfully. In this context, in the first part of this thesis, we investigated the human electromagnetic field (EMF) exposure in indoor and outdoor environments from fifth-generation (5G) downlink communications and compared its impacts with the present cellular technologies considering the features that the 5G will likely adopt. The second part focuses on mitigation of human exposure for both indoor and outdoor environments with two different methods adopted. Our simulation results suggest that while the impacts from 5G communications cross the regulatory borders for a very short separation distance between base stations (BSs) and user equipment (UE), the exposure level remains high throughout the network compared to the present systems. This work also highlights the significance of considering SAR for the measurement of exposure compliance in downlinks