Analysis of Millimeter-Wave Networks: Blockage, Antenna Directivity, Macrodiversity, and Interference

Due to its potential to support high data rates at low latency with reasonable interference isolation because of signal blockage at these frequencies, millimeter-wave (mmWave) communications has emerged as a promising solution for next-generation wireless networks. MmWave systems are characterized by the use of highly directional antennas and susceptibility to signal blockage by buildings and other obstructions, which significantly alter the propagation environment. The received power of each transmission depends on the direction the corresponding antennas point and whether the signal’s path is line-of-sight (LOS), non-LOS (i.e., partially blocked), or completely blocked. A key challenge in modeling blocking in mmWave networks is that, in actual networks, the blocking might be correlated. Such correlation arises, for example, when single transmitter tries to broadcast to pair of receivers that are close to each other, or more generally when they have a similar angle to the transmitter. In this situation, if the first receiver is blocked, it is likely that the second one is blocked, too. This dissertation explores four related but distinct issues associated with mmWave networks: 1) Analytical modeling of networks consisting of user devices and blockages with fixed or random, but independent, locations, 2) The careful characterization of correlated blocking and analysis of its impact on the performance of mmWave networks, 3) The proposed use of macrodiversity as an important strategy to mitigating correlated blocking in mmWave networks and the corresponding analysis, and 4) The proposed use of networks of unmanned aerial vehicles (UAVs) to provide connectivity in urban deployments. This work provides insight into the performance of variety of applications of mmWave communications, ranging from wireless personal area networks (WPAN), device-to-device networks, traditional terrestrial, cellular networks, and the UAV-based networks where the UAVs act as the cellular base stations. A common thread throughout this dissertation is the development of new tools based on stochastic geometry and their application to modeling and analysis. The analysis presented in this dissertation is general enough to find application beyond mmWave networks, for instance the results may also be applicable to systems that use free-space optical (FSO) signaling technologies

Technologies for urban and rural internet of things

Nowadays, application domains such as smart cities, agriculture or intelligent transportation, require communication technologies that combine long transmission ranges and energy efficiency to fulfill a set of capabilities and constraints to rely on. In addition, in recent years, the interest in Unmanned Aerial Vehicles (UAVs) providing wireless connectivity in such scenarios is substantially increased thanks to their flexible deployment. The first chapters of this thesis deal with LoRaWAN and Narrowband-IoT (NB-IoT), which recent trends identify as the most promising Low Power Wide Area Networks technologies. While LoRaWAN is an open protocol that has gained a lot of interest thanks to its simplicity and energy efficiency, NB-IoT has been introduced from 3GPP as a radio access technology for massive machine-type communications inheriting legacy LTE characteristics. This thesis offers an overview of the two, comparing them in terms of selected performance indicators. In particular, LoRaWAN technology is assessed both via simulations and experiments, considering different network architectures and solutions to improve its performance (e.g., a new Adaptive Data Rate algorithm). NB-IoT is then introduced to identify which technology is more suitable depending on the application considered. The second part of the thesis introduces the use of UAVs as flying Base Stations, denoted as Unmanned Aerial Base Stations, (UABSs), which are considered as one of the key pillars of 6G to offer service for a number of applications. To this end, the performance of an NB-IoT network are assessed considering a UABS following predefined trajectories. Then, machine learning algorithms based on reinforcement learning and meta-learning are considered to optimize the trajectory as well as the radio resource management techniques the UABS may rely on in order to provide service considering both static (IoT sensors) and dynamic (vehicles) users. Finally, some experimental projects based on the technologies mentioned so far are presented