Integration of A* and k-means clustering for star network design in environments considering obstacles

Abstract

Multi-level star is a widely adopted topology in many telecommunication systems, including wireless and wired networks. The optimization of this network topology, however, is not trivial, especially in the presence of obstacles. In this paper, we employ the well-known k-means clustering method but, instead of using a distance metric that neglects obstacles, we use the A* algorithm to find trajectories that allow circumventing obstacles. To assess the feasibility of the proposed approach, we apply it to 25 users arbitrarily located in a 20×20 uniform grid. This example indicates that by adopting A* to find obstacleaware trajectories, the mean distance between the users and their associated centroids is sensibly reduced.

Design of interdigital filters for millimeter-wave applications using artificial neural networks

“…Artificial Neural Networks (ANNs), a subset of machine learning, simulate biological learning through interconnected neurons. These neurons compute functions using input data and weights, enabling learning when connected with other neurons [5], [6], [7].…”