Modeling medium dense cellular network and evaluation of probability of coverage

Abstract

It is well known that cellular networks are continuously densifying at an enormous rate everywhere around the globe. Much work has been done in modeling ultra‐dense cellular (UDC) network. However, at present, there are no satisfactory models for medium dense cellular (MDC) network. Motivation of our work is to fill this gap in the literature. In particular, we propose to develop a model for the MDC network and derive the coverage probability. In this article, an optimization problem based on sparse dictionary learning (SDL) has been used to find a linear dependency between the UDC region and MDC region with respect to the base stations. A model for the MDC network is defined based on the linear dependency produced using SDL. The linear dependency allows us to define a relation connecting the distance metric between the MDC and UDC regions. Different terms in the expression for the equivalent distance metric have been identified, and the most relevant terms have been used to evaluate the coverage probability of the MDC network. Validation of our model has been done by comparing the correlation graph between the proposed model and the ideal model. Our proposed network model opens up the scope for more accurate modeling and analysis of medium‐dense cellular networks.