[Paper Review] Massive Wireless Energy Transfer: Enabling Sustainable IoT Towards 6G Era

Onel L. Alcaraz López, Hirley Alves+4 more|arXiv (Cornell University)|Dec 11, 2019

Energy Harvesting in Wireless NetworksEngineering119 references21 citations

TL;DR

This paper proposes massive wireless energy transfer (WET) using energy beamforming (EB) and distributed antenna systems (DAS) to enable sustainable, zero-energy IoT networks for the 6G era. It demonstrates that combining DAS with EB and leveraging average CSI significantly improves energy efficiency and scalability, especially in ultra-low-power, massive-device deployments.

ABSTRACT

Recent advances on wireless energy transfer (WET) make it a promising solution for powering future Internet of Things (IoT) devices enabled by the upcoming sixth generation (6G) era. The main architectures, challenges and techniques for efficient and scalable wireless powering are overviewed in this paper. Candidates enablers such as energy beamforming (EB), distributed antenna systems (DAS), advances on devices' hardware and programmable medium, new spectrum opportunities, resource scheduling and distributed ledger technology are outlined. Special emphasis is placed on discussing the suitability of channel state information (CSI)-limited/free strategies when powering simultaneously a massive number of devices. The benefits from combining DAS and EB, and from using average CSI whenever available, are numerically illustrated. The pros and cons of the state-of-the-art CSI-free WET techniques in ultra-low power setups are thoroughly revised, and some possible future enhancements are outlined. Finally, key research directions towards realizing WET-enabled massive IoT networks in the 6G era are identified and discussed in detail.

Motivation & Objective

• Address the critical challenge of powering massive, low-energy IoT devices sustainably in the 6G era.
• Overcome limitations in existing wireless energy transfer (WET) systems, particularly in scalability, energy efficiency, and deployment feasibility.
• Enable continuous, contact-free, and green operation of IoT devices through advanced WET architectures and hardware innovations.
• Investigate the viability of CSI-limited and CSI-free WET strategies for ultra-low-power, massive IoT deployments.
• Identify key research directions for integrating WET into future 6G networks, including energy harvesting, intelligent reflecting surfaces, and distributed ledger technology.

Proposed method

• Proposes energy beamforming (EB) with distributed antenna systems (DAS) to enhance spatial multiplexing and energy transfer efficiency to massive IoT devices.
• Introduces CSI-limited and CSI-free WET strategies, emphasizing average CSI utilization to reduce feedback overhead in low-complexity, ultra-low-power setups.
• Analyzes the integration of intelligent reflecting surfaces (IRS) with WET, focusing on passive beamforming and optimal IRS placement under limited CSI.
• Leverages machine learning (ML) and ray tracing for location prediction, beam alignment, and risk-aware deployment in blockage-prone mmWave environments.
• Proposes distributed ledger technology (DLT) and mobile edge computing (MEC) to enable secure, decentralized energy trading and resource scheduling in WET-enabled networks.
• Integrates ultra-low-power circuit design, on-chip intelligence, and energy-harvesting components (e.g., rectennas) into device packages to minimize energy loss and form factor.

Experimental results

Research questions

• RQ1 How can energy beamforming and distributed antenna systems be jointly optimized to scale WET for massive IoT networks in 6G?
• RQ2 What are the performance trade-offs of CSI-limited versus CSI-free WET strategies in ultra-low-power, massive-device deployments?
• RQ3 How can intelligent reflecting surfaces (IRS) be deployed effectively to enhance WET efficiency under passive, fully reflective constraints and limited CSI?
• RQ4 What role do location prediction, ML, and risk-aware clustering play in mitigating blockage and beam misalignment in mmWave WET?
• RQ5 How can green-powered base stations and decentralized energy trading via DLT enable sustainable, self-sustaining WET networks?

Key findings

• Combining DAS with energy beamforming significantly improves energy transfer efficiency and supports simultaneous powering of a massive number of IoT devices.
• Using average channel state information (CSI) instead of instantaneous CSI reduces feedback overhead and maintains high performance, especially in low-complexity, ultra-low-power setups.
• CSI-limited EB designs are viable and effective in practical scenarios with energy-constrained devices and passive IRS, enabling scalable WET deployment.
• IRS-based WET shows strong potential for enhancing coverage and beamforming gain, but requires careful optimization of IRS location, size, and number of reflective elements.
• mmWave WET is vulnerable to blockage and signal attenuation, but can be mitigated through hybrid radio access, DAS, and real-time human detection for safety.
• Sustainable WET is achievable by powering base stations with ambient or dedicated energy harvesting, supported by DLT-based energy trading and cooperative scheduling protocols.