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

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex

Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin

Relationship between earthquake fault triggering and societal behavior using ant colony optimization

In this analysis, we use the ant behaviour in simulating a framework for analysis of complex interplay amongst short time-scale deformation, long time- scale tectonics for positive stress coupling and slip interactions in earthquake genesis modeling. Using the proposed improved ant colony algorithm for global optimization the best solution ants within the search and the circulation of the optimal solution as the initial solution search, to expand its search, to avoid falling into local optimum  of  trigger zones analysis for earthquake occurrences. In order to validate the avalanche behaviour and corresponding nucleation we  best solution as the initial solution is adopted in order to widen searching scope to avoid getting into local optimum . In this proposed framework, an ant colony model is simulated to identify the physical framework of identifying trigger basins for the precursors to geodynamic model of propagation for precursory stress-strain signals. The disturbances at trigger basins cause the collapse of a subsystem leading to stress evolution and slip nucleation. Trigger basins help identify the zone of earthquake source nucleation as an index of ? and ? for strain analysis. The stress strain network can be interpreted by the increase in steady-state energy transmitted due to redistribution of stress accumulation into the earth tectonic framework. Sand pile behaviour model has been modeled through ant colony optimization for forecasting of likelihood time of triggering influences of lithosphere on the basis of critical zones of lithosphere where dump of elastic pressure is possible. The ant colony adaptive framework consisted of vertices representing the stress-strain component and edges, representing scored transformations for global coupling effects have been constructed for dynamic monitoring of stress and strain behaviour. Triggering basins serve as harbingers of large earthquake where stress-strain interactions have been analyzed by the quasi-static mechanics of seismic precursory stress-strain propagation in the crustal lithosphere. The study shows that dynamic variation of stress drop due to saved up pressure can be modeled by ant colony framework for steady state release due to trigger and global correlation framework. The simulation framework shows that with time, spatial triggering points can be negatively coupled and these interact with lesser impact, while positive coupling occurs only with more distant zones of stress generation for geodynamic frameworks, suggesting that the structural heterogeneities within the causative rocks associated with cracks and pores can dictate the pattern of stress – strain interactions and earthquake generating processes. Keywords: crack–porous, ant colony, geo-dynamical framework, stress-strain transmission, emergenc

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible