Statistical learning of geometric characteristics of wireless networks

Abstract

Motivated by the prediction of cell loads in cellular networks, we formulate the following new, fundamental problem of statistical learning of geometric marks of point processes: An unknown marking function, depending on the geometry of point patterns, produces characteristics (marks) of the points. One aims at learning this function from the examples of marked point patterns in order to predict the marks of new point patterns. To approximate (interpolate) the marking function, in our baseline approach, we build a statistical regression model of the marks with respect some local point distance representation. In a more advanced approach, we use a global data representation via the scattering moments of random measures, which build informative and stable to deformations data representation, already proven useful in image analysis and related application domains. In this case, the regression of the scattering moments of the marked point patterns with respect to the non-marked ones is combined with the numerical solution of the inverse problem, where the marks are recovered from the estimated scattering moments. Considering some simple, generic marks, often appearing in the modeling of wireless networks, such as the shot-noise values, nearest neighbour distance, and some characteristics of the Voronoi cells, we show that the scattering moments can capture similar geometry information as the baseline approach, and can reach even better performance, especially for non-local marking functions. Our results motivate further development of statistical learning tools for stochastic geometry and analysis of wireless networks, in particular to predict cell loads in cellular networks from the locations of base stations and traffic demand.

An Automated Network Slicing at Edge with Software Defined Networking and Network Function Virtualization: A Federated Learning Approach

“…Autoscaling is done on demand prediction based on the history of the request made for each resource type in particular domain. The slice load predictor uses statistical learning model [25] for the mean arrival rate of tra c pattern. Based on the statistics of the peak tra c rate and mean arrival rate, the future demand of the slice resources can be estimated and optimal resource provisioning can be done using the algorithm 1.…”