Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning

arXiv:2404.15374v1 Announce Type: cross
Abstract: Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent performance against complex channel environments, the computational complexity coming from processing high-dimensional features can be prohibitive for mobile applications. In this work, we design a novel positioning neural network (P-NN) that utilizes the minimum description features to substantially reduce the complexity of deep learning-based WP. P-NN's feature selection strategy is based on …

abstract algorithms applications arxiv complexity computational consistent cs.lg deep learning eess.sp environments feature features feature selection machine machine learning minimum mobile mobile applications performance processing solutions type wireless