Exploiting the ability of Self Organizing Networks for inter-cell interference coordination for emergency communications in cellular networks

Title from PDF of title page, viewed on June 15, 2015Thesis advisor: Cory BeardVitaIncludes bibliographic references (pages 56-57)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2014In the current scenario, radio planning of wireless cellular networks and analysis of radio performance should be agile because it is expected that in the near future we will be reaching to the point where there will be as many mobile devices as people in the world. So, there should be a rapid revolution in technology which can aid in the management of resources and maximization of throughput to satisfy users effectively. LTE and LTE-Advanced is designed to meet high bit rate service requirements; however, the initial challenge of the wireless channel, such as limited spectrum, leads to frequency reuse but also irrevocable interference. This thesis gives a holistic conspectus of interference coordination in LTE cellular systems utilizing the ability of Self Organizing Networks (SON). LTE uses a universal frequency reuse concept and the only interference observed in LTE is inter-cell interference. In a network where users are randomly distributed over three cells, it manages resources between the base stations by restricting some resource blocks for Cell Edge Users (CEU) of the neighboring cell and other resource blocks for Cell Center Users (CCU). This is done in a semi-static approach by taking into account the location of the user and varying channel conditions. Cell edge users and cell center users are distinguished based upon the SINR level. The management of the resources are regulated as per the user requirements and coordinated by the neighboring cells. The results have been simulated in two different ambiances viz., normal traffic and the emergency condition to show its performance in exigency. The throughput of the CCUs and CEUs in normal traffic has been compared. Also, the approach and results are shown to be highly reliable.Introduction -- Background -- Our work -- MATLAB code implementation -- Results and analysis -- Conclusion and future scop

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