Fundamentals of Wireless Information and Power Transfer: From RF Energy Harvester Models to Signal and System Designs
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Abstract (arXiv)
Radio waves carry both energy and information simultaneously. Nevertheless, Radio-Frequency (RF) transmission of these quantities have traditionally been treated separately. Currently, we are experiencing a paradigm shift in wireless network design, namely unifying wireless transmission of information and power so as to make the best use of the RF spectrum and radiations as well as the network infrastructure for the dual purpose of communicating and energizing. In this paper, we review and discuss recent progress on laying the foundations of the envisioned dual purpose networks by establishing a signal theory and design for Wireless Information and Power Transmission (WIPT) and identifying the fundamental tradeoff between conveying information and power wirelessly. We start with an overview of WIPT challenges and technologies, namely Simultaneous Wireless Information and Power Transfer (SWIPT),Wirelessly Powered Communication Network (WPCN), and Wirelessly Powered Backscatter Communication (WPBC). We then characterize energy harvesters and show how WIPT signal and system designs crucially revolve around the underlying energy harvester model. To that end, we highlight three different energy harvester models, namely one linear model and two nonlinear models, and show how WIPT designs differ for each of them in single-user and multi-user deployments. Topics discussed include rate-energy region characterization, transmitter and receiver architecture, waveform design, modulation, beamforming and input distribution optimizations, resource allocation, and RF spectrum use. We discuss and check the validity of the different energy harvester models and the resulting signal theory and design based on circuit simulations, prototyping and experimentation. We also point out numerous directions that are promising for future research.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Mar 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2021Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfercited by 10
- 2023Reliable and Efficient Data Collection in UAV-based IoT Networkscited by 3
- 2023On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learningcited by 0
- 2024Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning Approachcited by 0
- 2026AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Surveycited by 0
- 2026AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Survey22 Jun 2026
- 2024Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning Approach21 May 2024
- 2023On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning13 Dec 2023
- 2023Reliable and Efficient Data Collection in UAV-based IoT Networks9 Nov 2023
- 2021Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer24 Feb 2021