A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial

Beam alignment for millimeter wave vehicular communications

Millimeter wave (mmWave) has the potential to provide vehicles with high data rate communications that will enable a whole new range of applications. Its use, however, is not straightforward due to its challenging propagation characteristics. One approach to overcome the propagation challenge is the use of directional beams, but it requires a proper alignment and presents a challenging engineering problem, especially under the high vehicular mobility. In this dissertation, fast and efficient beam alignment solutions suitable for vehicular applications are developed. To better quantify the problem, first the impact of directional beams on the temporal variation of the channels is investigated theoretically. The proposed model includes both the Doppler effect and the pointing error due to mobility. The channel coherence time is derived, and a new concept called the beam coherence time is proposed for capturing the overhead of mmWave beam alignment. Next, an efficient learning-based beam alignment framework is proposed. The core of this framework is the beam pair selection methods that use side information (position in this case) and past beam measurements to identify promising beam directions and eliminate unnecessary beam training. Three offline learning methods for beam pair selection are proposed: two statistics-based and one machine learning-based methods. The two statistical learning methods consist of a heuristic and an optimal selection that minimizes the misalignment probability. The third one uses a learning-to-rank approach from the recommender system literature. The proposed approach shows an order of magnitude lower overhead than existing standard (IEEE 802.11ad) enabling it to support large arrays at high speed. Finally, an online version of the optimal statistical learning method is developed. The solution is based on the upper confidence bound algorithm with a newly introduced risk-aware feature that helps avoid severe misalignment during the learning. Along with the online beam pair selection, an online beam pair refinement is also proposed for learning to adapt the codebook to the environment to further maximize the beamforming gain. The combined solution shows a fast learning behavior that can quickly achieve positive gain over the exhaustive search on the original (and unrefined) codebook. The results show that side information can help reduce mmWave link configuration overhead.Electrical and Computer Engineerin

AI and IoT Meet Mobile Machines: Towards a Smart Working Site

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)

AI and IoT Meet Mobile Machines

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)

Direct communication radio Iinterface for new radio multicasting and cooperative positioning

Cotutela: Universidad de defensa UNIVERSITA’ MEDITERRANEA DI REGGIO CALABRIARecently, the popularity of Millimeter Wave (mmWave) wireless networks has increased due to their capability to cope with the escalation of mobile data demands caused by the unprecedented proliferation of smart devices in the fifth-generation (5G). Extremely high frequency or mmWave band is a fundamental pillar in the provision of the expected gigabit data rates. Hence, according to both academic and industrial communities, mmWave technology, e.g., 5G New Radio (NR) and WiGig (60 GHz), is considered as one of the main components of 5G and beyond networks. Particularly, the 3rd Generation Partnership Project (3GPP) provides for the use of licensed mmWave sub-bands for the 5G mmWave cellular networks, whereas IEEE actively explores the unlicensed band at 60 GHz for the next-generation wireless local area networks. In this regard, mmWave has been envisaged as a new technology layout for real-time heavy-traffic and wearable applications. This very work is devoted to solving the problem of mmWave band communication system while enhancing its advantages through utilizing the direct communication radio interface for NR multicasting, cooperative positioning, and mission-critical applications. The main contributions presented in this work include: (i) a set of mathematical frameworks and simulation tools to characterize multicast traffic delivery in mmWave directional systems; (ii) sidelink relaying concept exploitation to deal with the channel condition deterioration of dynamic multicast systems and to ensure mission-critical and ultra-reliable low-latency communications; (iii) cooperative positioning techniques analysis for enhancing cellular positioning accuracy for 5G+ emerging applications that require not only improved communication characteristics but also precise localization. Our study indicates the need for additional mechanisms/research that can be utilized: (i) to further improve multicasting performance in 5G/6G systems; (ii) to investigate sideline aspects, including, but not limited to, standardization perspective and the next relay selection strategies; and (iii) to design cooperative positioning systems based on Device-to-Device (D2D) technology

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications