An Online Kernel Scalar Quantization Scheme For Signal Classification

Distributed relay networking is one way of enabling connectivity between users that lack the necessary infrastructure to communicate with each other. An important advantage of such networks is the restoration of wireless communication coverage in the case of emergencies such as severe power outages or natural disasters. In this paper, we propose a system model consisting of a compress-and-forward relay network such that the data at a given relay node is quantized and broadcasted to the fusion center to predict its corresponding class label in an online fashion. In this context, we propose and study an online kernel scalar quantization learning strategy to estimate the decision function and associated empirical conditional probabilities to enhance the overall classification accuracy rate. In doing so, we devise a jointly optimum classification quantization approach that can find applications in a variety of settings in signal processing, machine learning, and communications.