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

Consciousness as Recursive, Spatiotemporal Self-Location

At the phenomenal level, consciousness arises in a consistently coherent fashion as a singular, unified field of recursive self-awareness (subjectivity) with explicitly orientational characteristics—that of a subject located both spatially and temporally in an egocentrically-extended domain. Understanding these twin elements of consciousness begins with the recognition that ultimately (and most primitively), cognitive systems serve the biological self-regulatory regime in which they subsist. The psychological structures supporting self-located subjectivity involve an evolutionary elaboration of the two basic elements necessary for extending self-regulation into behavioral interaction with the environment: an orientative reference frame which consistently structures ongoing interaction in terms of controllable spatiotemporal parameters, and processing architecture that relates behavior to homeostatic needs via feedback. Over time, constant evolutionary pressures for energy efficiency have encouraged the emergence of anticipative feedforward processing mechanisms, and the elaboration, at the apex of the sensorimotor processing hierarchy, of self-activating, highly attenuated recursively-feedforward circuitry processing the basic orientational schema independent of external action output. As the primary reference frame of active waking cognition, this recursive self-locational schema processing generates a zone of subjective self-awareness in terms of which it feels like something to be oneself here and now. This is consciousness-as-subjectivity

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure