Beyond Normal: Learning Spatial Density Models of Node Mobility
This addresses network optimization problems by improving density modeling, but it is incremental as it adapts existing mixture models to a specific domain.
The paper tackled modeling spatial density functions for mobile nodes on a disk to aid network design, proposing Möbius distributions for flexibility and symmetry, and found they outperform Gaussian mixtures but are more fragile to learn.
Learning models of complex spatial density functions, representing the steady-state density of mobile nodes moving on a two-dimensional terrain, can assist in network design and optimization problems, e.g., by accelerating the computation of the density function during a parameter sweep. We address the question of applicability for off-the-shelf mixture density network models for the description of mobile node density over a disk. We propose the use of Möbius distributions to retain symmetric spatial relations, yet be flexible enough to capture changes as one radially traverses the disk. The mixture models for Möbius versus Gaussian distributions are compared and the benefits of choosing Möbius distributions become evident, yet we also observe that learning mixtures of Möbius distributions is a fragile process, when using current tools, compared to learning mixtures of Gaussians.