Ricerc@Sapienza

Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising technology for massive machine type communication. Given that its deployment is rapidly progressing worldwide, measurement campaigns and performance analyses are needed to better understand the system and move toward its enhancement. With this aim, this article presents a large-scale measurement campaign and empirical analysis of NB-IoT on operational networks, and discloses valuable insights in terms of deployment strategies and radio coverage performance.

We target the maximization of the operator profitability in a LTE cellular network with e-NodeBs (eNBs) exploiting Sleep Mode (SM) power states. The profitability is composed of different terms, namely: the electricity bill due to the eNBs energy consumption, the maintenance costs due to the application of power states to the eNBs, and the revenues from providing a given throughput to users. After showing that all these components are strictly interdependent, we formulate the problem of maximizing the profitability for a set of eNBs.

This paper deals with the adoption of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations providing video streaming services within a cellular macro area. We devise a Q-learning based UAV flight planning algorithm aimed at improving the Quality of Experience (QoE) of video users. Specifically, the proposed algorithm, herein denoted as Q-SQUARE, leverages the well-established Q-learning algorithm by introducing a reward related to a key QoE metric that is the video segment delay.