An in-depth analysis of future sixth generation networks with integration of aerial platforms

The spread of new forms of technologies such as the Internet of Things (IoT) and Information and Communication Technology (ICT) will affect the future of the mobile wireless connections. In the society of the future everything and everyone will be interconnected: it is expected that by 2025 the number of connected devices worldwide will rise to 75 billion. This increase translates into rapid growth in mobile traffic, which poses two major problems. First, the new mobile networks require more spectrum and vastly more spectrally efficient technologies. Second, this growth raises doubts about the sustainability of mobile communications. Industrial and academic synergies have begun to conceptualize the next generation of wireless communication systems (i.e., sixth generation, (6G)) to cope with these issues. However, the practical implementation of this new wireless communication system presents some technical challenges. To address some of these issues, 6G research is currently focusing on the development of Non-Terrestrial Networks (NTNs) to promote ubiquitous and high-capacity global connectivity. With NTNs it is envisioned a three-dimensional vertical heterogeneous architecture in which terrestrial infrastructures are complemented by non-terrestrial stations including Unmanned Aerial Vehicles (UAVs), High Altitude Platforms Stations (HAPSs) and satellites. In this thesis, we consider a portion of a future generation Radio Access Network (RAN), in which we imagine integrating the ground network infrastructure of a densely populated area like the city of Milan (Italy) and its suburbs, with an air network formed by one HAPS. We plan to offer coverage for 16 traffic zones that include urban and suburban areas of the city of Milan, each different from the others in terms of surface extension, typical activities and traffic patterns. From each traffic zone, we consider a sample cluster of base stations (BSs) powered with photovoltaic (PV) panels, equipped with energy storage units, and a connection to the power grid. Our study examines the impact of different strategies, such as Resource on Demand (RoD) and HAPS Offloading strategies, to reduce the cluster energy consumption and improve the Quality of Service (QoS) by adapting the cluster capacity to traffic conditions. Using a detailed simulator, the results show that by applying the RoD and HAPS Offloading strategies at the same time, with the presence of Renewable Energy (RE) supply both for the ground infrastructure and for the HAPS, significant savings can be obtained in both energetic and economic terms. Moreover the use of Machine Learning (ML) algorithms allow to adapt the network capacity according to the traffic, increasing the QoS.