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dc.contributor.authorClerckx, Bruno
dc.contributor.authorHuang, Kaibin
dc.contributor.authorVarshney, Lav
dc.contributor.authorUlukus, Sennur
dc.contributor.authorAlouini, Mohamed-Slim
dc.identifier.citationClerckx, B., Huang, K., Varshney, L., Ulukus, S., & Alouini, M. (2021). Wireless Power Transfer for Future Networks: Signal Processing, Machine Learning, Computing, and Sensing. IEEE Journal of Selected Topics in Signal Processing, 1–1. doi:10.1109/jstsp.2021.3098478
dc.description.abstractWireless power transfer (WPT) is an emerging paradigm that will enable using wireless to its full potential in future networks, not only to convey information but also to deliver energy. Such networks will enable trillions of future low-power devices to sense, compute, connect, and energize anywhere, anytime, and on the move. The design of such future networks brings new challenges and opportunities for signal processing, machine learning, sensing, and computing so as to make the best use of the RF spectrum, radiation, and network infrastructure in providing cost-effective and realtime power supplies to wireless devices and enable wirelesspowered applications. In this paper, we first review recent signal processing techniques to make WPT and wireless information and power transfer (WIPT) as efficient as possible. Topics include high-power amplifier and energy harvester nonlinearities, active and passive beamforming, intelligent reflecting surfaces, receive combining with multi-antenna harvester, modulation, coding, waveform, large-scale (massive) multiple-input multipleoutput (MIMO), channel acquisition, transmit diversity, multiuser power region characterization, coordinated multipoint, and distributed antenna systems. Then, we overview two different design methodologies: the model and optimize approach relying on analytical system models, modern convex optimization, and communication/ information theory, and the learning approach based on data-driven end-to-end learning and physics-based learning. We discuss the pros and cons of each approach, especially when accounting for various nonlinearities in wireless-powered networks, and identify interesting emerging opportunities for the approaches to complement each other. Finally, we identify new emerging wireless technologies where WPT may play a key rolewireless-powered mobile edge computing and wirelesspowered sensingarguing WPT, communication, computation, and sensing must be jointly designed.
dc.description.sponsorshipThis work has been partially supported by the EPSRC of UK under grant EP/P003885/1 and EP/R511547/1.
dc.rights(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.titleWireless Power Transfer for Future Networks: Signal Processing, Machine Learning, Computing, and Sensing
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE Journal of Selected Topics in Signal Processing
dc.contributor.institutionElectrical and Electronic Engineering, Imperial College London, London, +44 20 7594 6234, United Kingdom of Great Britain and Northern Ireland, SW7 2AZ
dc.contributor.institutionHong Kong, Hong Kong,
dc.contributor.institutionElectrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States, 61801
dc.contributor.institutionECE, Univ. of Maryland, College Park, Maryland, United States, 20742
kaust.personAlouini, Mohamed-Slim

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