  Wireless 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 radiations, spectrum, and
network infrastructure in providing cost-effective and real-time power supplies
to wireless devices and enable wireless-powered applications. In this paper, we
first review recent signal processing techniques to make WPT and wireless
information and power transfer as efficient as possible. Topics include power
amplifier and energy harvester nonlinearities, active and passive beamforming,
intelligent reflecting surfaces, receive combining with multi-antenna
harvester, modulation, coding, waveform, massive MIMO, channel acquisition,
transmit diversity, multi-user 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 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 role -- wireless-powered mobile edge computing and
wireless-powered sensing -- arguing WPT, communication, computation, and
sensing must be jointly designed.
