Enhanced IoT base Station Performance Assessment using Product Density Modeling in Non-Stationary Traffic Scenarios

This research has focused on the issues related to time-varying traffic in IoT networks, especially for 5G-types environments underpinned by massive machine-type communications (mMTC) and ultra-reliable low-latency communication (uRLLC). We propose an innovative method for modeling network performance dynamics over time, capturing fluctuations in offered traffic and delay under non-stationary conditions with a novel approach based on Product Densities. With a simulation-based evaluation, we show the accuracy of our method for base station performance assessment in comparison to traditional methods considering Poisson and non-homogeneous Poisson arrival models. Information gathered this way is necessary for better and more efficient IoT network infrastructure in those high volume, low latency applications.