337 research outputs found

    Integration of hybrid networks, AI, Ultra Massive-MIMO, THz frequency, and FBMC modulation toward 6g requirements : A Review

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    The fifth-generation (5G) wireless communications have been deployed in many countries with the following features: wireless networks at 20 Gbps as peak data rate, a latency of 1-ms, reliability of 99.999%, maximum mobility of 500 km/h, a bandwidth of 1-GHz, and a capacity of 106 up to Mbps/m2. Nonetheless, the rapid growth of applications, such as extended/virtual reality (XR/VR), online gaming, telemedicine, cloud computing, smart cities, the Internet of Everything (IoE), and others, demand lower latency, higher data rates, ubiquitous coverage, and better reliability. These higher requirements are the main problems that have challenged 5G while concurrently encouraging researchers and practitioners to introduce viable solutions. In this review paper, the sixth-generation (6G) technology could solve the 5G limitations, achieve higher requirements, and support future applications. The integration of multiple access techniques, terahertz (THz), visible light communications (VLC), ultra-massive multiple-input multiple-output ( μm -MIMO), hybrid networks, cell-free massive MIMO, and artificial intelligence (AI)/machine learning (ML) have been proposed for 6G. The main contributions of this paper are a comprehensive review of the 6G vision, KPIs (key performance indicators), and advanced potential technologies proposed with operation principles. Besides, this paper reviewed multiple access and modulation techniques, concentrating on Filter-Bank Multicarrier (FBMC) as a potential technology for 6G. This paper ends by discussing potential applications with challenges and lessons identified from prior studies to pave the path for future research

    Variations on bayesian optimization applied to numerical flow simulations

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    Bayesian Optimization (BO) has recently regained interest in optimization problems involving expensive black-box objective functions. Several variants have been proposed in the literature, such as including gradient and/or multi-fidelity information, and it has been extended to multi-objective optimization problems. Despite its recent applications to numerical flow simulations, the efficiency of this method and its variants remains to be characterized in typical applications involving canonical flows. In this work, the efficiency of classical BO and alternative derivative-free methods is compared on a simplified flow case, i.e. drag reduction in the two-dimensional flow around a cylinder. The application of BO to complex flows is then showcased by considering a three-dimensional case at Reynolds number Re = 3900. Next, the performance of BO with gradient and/or multi-fidelity information is investigated for global modelling and optimization on typical benchmark objective functions and on the cylinder case at Re = 200. Finally, an algorithm combining dimension reduction and Multi-objective Bayesian Optimization (MOBO) is proposed. It is found that BO was more efficient than other derivative-free alternatives and showed promising results on the three-dimensional cylinder at Re = 3900 by reducing drag by 23 %. The performance of the algorithm was further improved when multi-fidelity and/or gradient information was included, both for modelling and optimization. Including gradient information on the low-fidelity model was useful for global modelling and to decrease rapidly the objective function in a BO framework. On the contrary, adding derivative information on the high-fidelity model generally gave the most accurate approximation of the minimum but was inefficient for global modelling when the computational cost of the gradient was high. Finally, the developed algorithm combining dimension reduction and MOBO enabled us to obtain more precise and diverse minima.136 página

    Machine Learning Empowered Reconfigurable Intelligent Surfaces

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    Reconfigurable intelligent surfaces (RISs) or known as intelligent reflecting surfaces (IRSs) have emerged as potential auxiliary equipment for future wireless networks, which attracts extensive research interest in their characteristics, applications, and potential. RIS is a panel surface equipped with a number of reflective elements, which can artificially modify the propagation environment of the electrogenic signals. Specifically, RISs have the ability to precisely adjust the propagation direction, amplitude, and phase-shift of the signals, providing users with a set of cascaded channels in addition to direct channels, and thereby improving the communication performances for users. Compared with other candidate technologies such as active relays, RIS has advantages in terms of flexible deployment, economical cost, and high energy efficiency. Thus, RISs have been considered a potential candidate technique for future wireless networks. In this thesis, a wireless network paradigm for the sixth generation (6G) wireless networks is proposed, where RISs are invoked to construct smart radio environments (SRE) to enhance communication performances for mobile users. In addition, beyond the conventional reselecting-only RIS, a novel model of RIS is originally proposed, namely, simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS splits the incident signal into transmitted and reflected signals, making full utilization of them to generate 360360^{\circ} coverage around the STAR-RIS panel, improving the coverage of the RIS. In order to fully exert the channel domination and beamforming ability of the RISs and STAR-RSIs to construct SREs, several machine learning algorithms, including deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) approaches are developed to optimize the communication performance in respect of sum data rate or energy efficiency for the RIS-assisted networks. Specifically, several problems are investigated including 1) the passive beamforming problem of the RIS with consideration of configuration overhead is resolved by a DL and a DRL algorithm, where the time overhead of configuration of RIS is successfully reduced by the machine learning algorithms. Consequently, the throughput during a time frame improved 95.2%95.2\% by invoking the proposed algorithms; 2) a novel framework of mobile RISs-enhanced indoor wireless networks is proposed, and a FL enhanced DRL algorithm is proposed for the deployment and beamforming optimization of the RIS. The average throughput of the indoor users severed by the mobile RIS is improved 15.1%15.1\% compared to the case of conventional fixed RIS; 3) A STAR-RIS assisted multi-user downlink multiple-input single-output (MISO) communication system is investigated, and a pair of hybrid reinforcement learning algorithms are proposed for the hybrid control of the transmitting and reflecting beamforming of the STAR-RIS, which ameliorate 7%7\% of the energy efficiency of the STAR-RIS assisted networks; 4) A tile-based low complexity beamforming approach is proposed for STAR-RISs, and the proposed tile-based beamforming approach is capable of achieving homogeneous data rate performance with element-based beamforming with appreciable lower complexity. By designing and operating the computer simulation, this thesis demonstrated 1) the performance gain in terms of sum data rate or energy efficiency by invoking the proposed RIS in the wireless communication networks; 2) the data rate or energy efficient performance gain of the proposed STAR-RIS compared to the existing reflecting-only RIS; 3) the effect of the proposed machine learning algorithms in terms of convergence rate, optimality, and complexity compared to the benchmarks of existing algorithms

    Video Conferencing: Infrastructures, Practices, Aesthetics

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    The COVID-19 pandemic has reorganized existing methods of exchange, turning comparatively marginal technologies into the new normal. Multipoint videoconferencing in particular has become a favored means for web-based forms of remote communication and collaboration without physical copresence. Taking the recent mainstreaming of videoconferencing as its point of departure, this anthology examines the complex mediality of this new form of social interaction. Connecting theoretical reflection with material case studies, the contributors question practices, politics and aesthetics of videoconferencing and the specific meanings it acquires in different historical, cultural and social contexts

    Applications

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    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

    Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

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    This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions

    Towards low complexity matching theory for uplink wireless communication systems

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    Millimetre wave (mm-Wave) technology is considered a promising direction to achieve the high quality of services (QoSs) because it can provide high bandwidth, achieving a higher transmission rate due to its immunity to interference. However, there are several limitations to utilizing mm-Wave technology, such as more extraordinary precision hardware is manufactured at a higher cost because the size of its components is small. Consequently, mm-Wave technology is rarely applicable for long-distance applications due to its narrow beams width. Therefore, using cell-free massive multiple input multiple output (MIMO) with mm-Wave technology can solve these issues because this architecture of massive MIMO has better system performance, in terms of high achievable rate, high coverage, and handover-free, than conventional architectures, such as massive MIMO systems’ co-located and distributed (small cells). This technology necessitates a significant amount of power because each distributed access point (AP) has several antennas. Each AP has a few radio frequency (RF) chains in hybrid beamforming. Therefore more APs mean a large number of total RF chains in the cell-free network, which increases power consumption. To solve this problem, deactivating some antennas or RF chains at each AP can be utilized. However, the size of the cell-free network yields these two options as computationally demanding. On the other hand, a large number of users in the cell-free network causes pilot contamination issue due to the small length of the uplink training phase. This issue has been solved in the literature based on two options: pilot assignment and pilot power control. Still, these two solutions are complex due to the cell-free network size. Motivated by what was mentioned previously, this thesis proposes a novel technique with low computational complexity based on matching theory for antenna selection, RF chains activation, pilot assignment and pilot power control. The first part of this thesis provides an overview of matching theory and the conventional massive MIMO systems. Then, an overview of the cell-free massive MIMO systems and the related works of the signal processing techniques of the cell-free mm-Wave massive MIMO systems to maximize energy efficiency (EE), are provided. Based on the limitations of these techniques, the second part of this thesis presents a hybrid beamforming architecture with constant phase shifters (CPSs) for the distributed uplink cell-free mm-Wave massive MIMO systems based on exploiting antenna selection to reduce power consumption. The proposed scheme uses a matching technique to obtain the number of selected antennas which can contribute more to the desired signal power than the interference power for each RF chain at each AP. Therefore, the third part of this thesis solves the issue of the huge complexity of activating RF chains by presenting a low-complexity matching approach to activate a set of RF chains based on the Hungarian method to maximize the total EE in the centralized uplink of the cell-free mm-Wave massive MIMO systems when it is proposed hybrid beamforming with fully connected phase shifters network. The pilot contamination issue has been discussed in the last part of this thesis by utilizing matching theory in pilot assignment and pilot power control design for the uplink of cell-free massive MIMO systems to maximize SE. Firstly, an assignment optimization problem has been formulated to find the best possible pilot sequences to be inserted into a genetic algorithm (GA). Therefore, the GA will find the optimal solution. After that, a minimum-weighted assignment problem has been formulated regarding the power control design to assign pilot power control coefficients to the quality of the estimated channel. Then, the Hungarian method is utilized to solve this problem. The simulation results of the proposed matching theory for the mentioned issues reveal that the proposed matching approach is more energy-efficient and has lower computational complexity than state-of-the-art schemes for antenna selection and RF chain activation. In addition, the proposed matching schemes outperform the state-of-the-art techniques concerning the pilot assignment and the pilot power control design. This means that network scalability can be guaranteed with low computational complexity

    BepiColombo Science Investigations During Cruise and Flybys at the Earth, Venus and Mercury

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    The dual spacecraft mission BepiColombo is the first joint mission between the European Space Agency (ESA) and the Japanese Aerospace Exploration Agency (JAXA) to explore the planet Mercury. BepiColombo was launched from Kourou (French Guiana) on October 20th, 2018, in its packed configuration including two spacecraft, a transfer module, and a sunshield. BepiColombo cruise trajectory is a long journey into the inner heliosphere, and it includes one flyby of the Earth (in April 2020), two of Venus (in October 2020 and August 2021), and six of Mercury (starting from 2021), before orbit insertion in December 2025. A big part of the mission instruments will be fully operational during the mission cruise phase, allowing unprecedented investigation of the different environments that will encounter during the 7-years long cruise. The present paper reviews all the planetary flybys and some interesting cruise configurations. Additional scientific research that will emerge in the coming years is also discussed, including the instruments that can contribute
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