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Wiley|Wiley-IEEE Press
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Comprehensive reference text for the development of advanced antenna systems for 5G and beyond Smart Antennas for 5G+ addresses the transformation that simple, conventional antennas have gone through to fulfill the complex and challenging performance expectations of 5G wireless communications and beyond. It also describes the 5G physical layer including the gNodeB, basics of the propagation channel, and user equipment (UE) specific antenna aspects. The authors present various broadband antennas covering mmW bands for 5G+ and B5G applications, explore recent developments in array calibration and plane wave generators, and consider future challenges for 5G+ radio system testing. Novel key performance indicators in which not only conventional RF parameters, but also other parameters and overall radio performance, are examined. The huge shrinkage which was prioritized in 4G modules are now combined with extremely versatile and precise beamforming capacities, thermal mitigation, and link budget compensation design strategies that bring antennas into a new era. As such, antenna integration, flexible and 3D printed elements, the use of metamaterials, and advanced testing of new features are key elements of this book. Additional topics covered in Smart Antennas for 5G+ include: Beamforming, beam-steering, power control, cell breathing, and many other 5G concepts that make the evaluation of a 5G antenna structure a complex process Multi-probe anechoic chamber, reverberation chamber, and wireless cable solutions, RF testing of the 5G antenna systems, and small-cell repeaters for 5G/B5G beamforming Powering and sensing applications in 5G+ printed electronic steerable antenna arrays for reconfigurable intelligent surfaces that stem from metasurfaces 3GPP/ITU standards and models for 5G MIMO base station antennas, propagation channel modeling, and link budget considerations The connection between complex but realistically used designs and the way testing has evolved to determine the value of novel designs makes Smart Antennas for 5G+ a must-have resource for advanced engineers as well as newcomers in the field of 5G wireless communications.
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Model Optimization Methods for Efficient and Edge AI
Collectif
- Wiley-IEEE Press
- 6 Novembre 2024
- 9781394219209
Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problemsGenerating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablersCompressing AI models so that computational, memory, storage, and network requirements can be substantially reducedAddressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous dataOvercoming cyberattacks on mission-critical software systems by leveraging federated learning Written in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, Model Optimization Methods for Efficient and Edge AI is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders.