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

Advanced Information Systems and Technologies

This book comprises the proceedings of the VI International Scientific Conference “Advanced Information Systems and Technologies, AIST-2018”. The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing, computer networking and telecomunications, modern methods and information technologies of sustainable development. They will be useful for students, graduate students, researchers who interested in computer science

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc