Algoritmos de aprendizado de máquina para coordenação de interferência entre células

The current LTE and LTE-A deployments require larger efforts to achieve the radio resource management. This, due to the increase of users and the constantly growing demand of services. For this reason, the automatic optimization is a key point to avoid issues such as the inter-cell interference. This paper presents several proposals of machine-learning algorithms focused on this automatic optimization problem. The research works seek that the cellular systems achieve their self-optimization, a key concept within the self-organized networks, where the main objective is to achieve that the networks to be capable to automatically respond to the particular needs in the dynamic network traffic scenarios.Los despliegues actuales de LTE y LTE-A requieren mayor esfuerzo para la gestión de recursos radio debido al incremento de usuarios y a la gran demanda de servicios; en ese escenario, la optimización automática es un punto clave para evitar problemas como la interferencia inter-celda. El presente trabajo recopila propuestas de algoritmos de aprendizaje automático [machine learning] enfocados en resolver este problema. Las investigaciones buscan que los sistemas celulares consigan su auto-optimización, un concepto que se enmarca dentro del área de redes auto-organizadas [Self-Organized Networks, SON], cuyo objetivo es lograr que las redes respondan de forma automática a las necesidades de los escenarios dinámicos de tráfico de red.As implantações atuais de LTE e LTE-A exigem maior esforço para o gerenciamento de recursos rádio devido ao aumento de usuários e à alta demanda por serviços, neste cenário a otimização automática é um ponto-chave para evitar problemas como a interferência entre células. O presente trabalho coleta propostas de algoritmos de aprendizado automáticos focados na resolução deste problema. A pesquisa busca que os sistemas celulares alcancem a sua auto-otimização, um conceito que faz parte das redes auto-organizadas (Self-Organizing Networks, SON), cujo objetivo é garantir que as redes respondam automaticamente às necessidades dos cenários dinâmicos do tráfego de rede

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

Packet Scheduling Algorithms in LTE/LTE-A cellular Networks: Multi-agent Q-learning Approach

Spectrum utilization is vital for mobile operators. It ensures an efficient use of spectrum bands, especially when obtaining their license is highly expensive. Long Term Evolution (LTE), and LTE-Advanced (LTE-A) spectrum bands license were auctioned by the Federal Communication Commission (FCC) to mobile operators with hundreds of millions of dollars. In the first part of this dissertation, we study, analyze, and compare the QoS performance of QoS-aware/Channel-aware packet scheduling algorithms while using CA over LTE, and LTE-A heterogeneous cellular networks. This included a detailed study of the LTE/LTE-A cellular network and its features, and the modification of an open source LTE simulator in order to perform these QoS performance tests. In the second part of this dissertation, we aim to solve spectrum underutilization by proposing, implementing, and testing two novel multi-agent Q-learning-based packet scheduling algorithms for LTE cellular network. The Collaborative Competitive scheduling algorithm, and the Competitive Competitive scheduling algorithm. These algorithms schedule licensed users over the available radio resources and un-licensed users over spectrum holes. In conclusion, our results show that the spectrum band could be utilized by deploying efficient packet scheduling algorithms for licensed users, and can be further utilized by allowing unlicensed users to be scheduled on spectrum holes whenever they occur