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Abstract: AbstractThe International Mobile Telecommunications (IMT)-2030 framework recently adopted by the International Telecommunication Union Radiocommunication Sector (ITU-R) envisions 6G networks to deliver intelligent, seamless connectivity that supports reliable, sustainable, and resilient communications. To achieve this vision, Non-Terrestrial Networks (NTN) represent a significant advancement by extending connectivity beyond the Earth's surface. These networks integrate advanced communication technologies that go beyond conventional terrestrial infrastructure, enabling comprehensive global connectivity across domains such as the Internet, Internet of Things (IoT), navigation, disaster recovery, remote access, Earth observation, and even scientific initiatives like interplanetary communication.Recent developments in the 3rd Generation Partnership Project (3GPP) Releases 17-19, particularly within the Radio Access Network (RAN)4 working group addressing satellite and cellular spectrum sharing and RAN2 enhancing New Radio (NR)/IoT for NTN, highlight the critical role NTN is set to play in the evolution of 6G standards. The integration of advanced signal processing, edge and cloud computing, and Deep Reinforcement Learning (DRL) for Low Earth Orbit (LEO) satellites and aerial platforms, such as Uncrewed Aerial Vehicles (UAV) and high-, medium-, and low-altitude platform stations, has revolutionized the convergence of space, aerial, and Terrestrial Networks (TN). Artificial Intelligence (AI)-powered deployments for NTN and NTN-IoT, combined with Next Generation Multiple Access (NGMA) technologies, have dramatically reshaped global connectivity.This monograph provides a comprehensive exploration of emerging NTN-based 6G wireless networks, covering vision, alignment with 5G-Advanced and 6G standards, key principles, trends, challenges, real-world applications, and novel problem solving frameworks. It examines essential enabling technologies like AI for NTN (LEO satellites and aerial platforms), DRL, edge computing for NTN, AI for NTN trajectory optimization, Reconfigurable Intelligent Surfaces (RIS)-enhanced NTN, and robust Multiple-Input-Multiple-Output (MIMO) beamforming. Furthermore, it addresses interference management through NGMA, including Rate-Splitting Multiple Access (RSMA) for NTN, and the use of aerial platforms for access, relay, and fronthaul/backhaul connectivity.Suggested CitationMuhammad Ali Jamshed, Aryan Kaushik, Sanaullah Manzoor, Muhammad Zeeshan Shakir, Jaehyup Seong, Mesut Toka, Wonjae Shin and Malte Schellmann (2025), "A Tutorial on Non-Terrestrial Networks: Towards Global and Ubiquitous 6G Connectivity", Foundations and Trends® in Networking: Vol. 14: No. 3, pp 160-253. http://dx.doi.org/10.1561/1300000072 PubDate: Wed, 12 Feb 2025 00:00:00 +010
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Abstract: AbstractThe 5th generation (5G) of wireless systems is being deployedwith the aim to provide many sets of wireless communicationservices, such as low data rates for a massive amountof devices, broadband, low latency, and industrial wirelessaccess. Such an aim is even more complex in the next generationwireless systems (6G) where wireless connectivityis expected to serve any connected intelligent unit, such assoftware robots and humans interacting in the metaverse,autonomous vehicles, drones, trains, or smart sensors monitoringcities, buildings, and the environment. Because ofthe wireless devices will be orders of magnitude denser thanin 5G cellular systems, and because of their complex qualityof service requirements, the access to the wireless spectrumwill have to be appropriately shared to avoid congestion,poor quality of service, or unsatisfactory communicationdelays. Spectrum sharing methods have been the objectiveof intense study through model-based approaches, such asoptimization or game theories. However, these methods mayfail when facing the complexity of the communication environmentsin 5G, 6G, and beyond. Recently, there has beensignificant interest in the application and development ofdata-driven methods, namely machine learning methods, tohandle the complex operation of spectrum sharing. In thissurvey, we provide a complete overview of the state-of-theartof machine learning for spectrum sharing. First, we mapthe most prominent methods that we encounter in spectrumsharing. Then, we show how these machine learning methodsare applied to the numerous dimensions and sub-problemsof spectrum sharing, such as spectrum sensing, spectrumallocation, spectrum access, and spectrum handoff. We alsohighlight several open questions and future trends.Suggested CitationFrancisco R. V. Guimarães, José Mairton B. da Silva Jr., Charles Casimiro Cavalcante, Gabor Fodor, Mats Bengtsson and Carlo Fischione (2024), "Machine Learning for Spectrum Sharing: A Survey", Foundations and Trends® in Networking: Vol. 14: No. 1-2, pp 1-159. http://dx.doi.org/10.1561/1300000073 PubDate: Wed, 06 Nov 2024 00:00:00 +010
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