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Massive Wireless Energy Transfer: Enabling Sustainable IoT Towards 6G Era

Onel L. Alcaraz López, Hirley Alves, Richard Demo Souza, Samuel Montejo-Sánchez, Evelio M. García Fernández, Matti Latva-aho

arXiv:1912.05322v2cs.NIcs.PF

TL;DR

Future 6G IoT networks need sustainable ways to power many low-power devices, motivating wireless energy transfer as an alternative to battery servicing and replacement. This paper surveys WET architectures, enabling techniques, CSI strategies, and research challenges, and reports that optimized DAS and combined DAS–EB can improve performance or reduce deployment costs.

  • Problem

    Future 6G IoT networks will require sustainable, steady powering for many low-cost, low-power devices, while battery servicing or replacement can be costly or impossible in some environments.

  • Method

    The paper overviews WET architectures, applications, enabling technologies, CSI-limited and CSI-free strategies, scheduling, and distributed ledger techniques, while reviewing associated challenges.

  • Results

    The paper reports that intelligently deployed DAS improves performance over collocated EB or unoptimized DAS, while combining DAS and EB can reduce deployment costs without significantly compromising performance.

  • Takeaways & Limitations

    WET can support sustainable IoT by simplifying maintenance, extending device durability, reducing battery-waste emissions, and enabling miniaturized, multi-user hardware.

  • Takeaways & Limitations

    Massive WET remains constrained by CSI acquisition, beam-alignment and attenuation challenges, and the limited powering capacity of passive IRS deployments.

Abstract

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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.

I. INTRODUCTION

6G targets sustainable, scalable connectivity for massive low-power IoT, but uninterrupted device operation remains difficult. The introduction presents energy harvesting, especially RF-WET, as a contact-free approach with scalability and deployment advantages.

  • 6G targets a green society with stringent requirements for security, throughput, sensing, dependability, scalability, and energy efficiency.
  • Massive IoT deployments lack mature solutions for powering devices and maintaining uninterrupted operation.
  • Energy harvesting can wirelessly recharge or replace batteries, simplifying maintenance and increasing device durability through contact-free operation.
  • Ambient EH uses environmental energy, whereas dedicated EH uses purposeful transmissions from dedicated sources.
  • Ambient EH can have limited service guarantees because temporal, geographical, and environmental conditions affect availability.
  • RF-WET is compared with capacitive, inductive, and magnetic coupling across coverage, form factor, harvestable energy, multi-user support, and mobility.

B. Scope and Contributions of this Work

The paper surveys massive WET for 6G IoT, covering architectures, enabling technologies, and challenges with particular emphasis on CSI-limited and CSI-free operation. It reports benefits from optimized DAS, DAS–EB integration, and average-CSI-based beamforming while identifying practical deployment and channel limitations.

  • RF-based EH supports small-form-factor devices by reusing RF circuitry for communications and energy harvesting.
  • RF-EH provides native multi-user support because the same RF signals can be harvested simultaneously by several devices.
  • The paper overviews WET features, architectures, applications, enablers, and challenges for powering massive IoT deployments in the 6G era.
  • The paper reviews EB, DAS, hardware, programmable media, spectrum, scheduling, and DLT as candidate enablers for scalable WET.
  • Optimized DAS improves performance over collocated EB or unoptimized DAS with the same transmit antennas and power.
  • Combining DAS and EB can reduce deployment costs without significantly compromising system performance.
  • CSI acquisition is a strong mWET limitation, motivating average-CSI and CSI-free approaches for massive low-power deployments.

II. ARCHITECTURE & APPLICATIONS OF WET SYSTEMS

WET is being integrated into future 6G wireless systems to support IoT, using RF energy-harvesting hardware whose components optimize reception, rectification, filtering, and power delivery.

  • WET is expected to become increasingly integrated with wireless systems as 6G expands industrial and IoT services.
  • An RF-EH module typically includes receive antennas, a matching network and bandpass filter, a rectifying circuit, and a low-pass filter.These elements respectively receive RF energy, maximize transfer, convert RF to usable power, and remove unwanted frequencies.
  • The RF-EH module can harvest from single or multiple frequency bands, while its capacitors provide short-duration reserve power when RF energy is unavailable.
  • CMOS diode-connected transistors improve energy-harvesting efficiency at lower powers and suit compact, low-cost applications such as RFID.Rectenna-based designs are preferable when larger power densities are available.

B. WET and Information Transmission

WET-enabled networks combine energy transfer with wireless information transmission through infrastructure-based or infrastructure-less architectures, with in-band and out-of-band designs offering different trade-offs.

  • WPCN separates WET and WIT into phases, whereas SWIPT performs wireless energy and information transfer in the same link direction.WET may require longer operation than WIT because devices must harvest usable energy.
  • Infrastructure-based networks comprise information gateways, power beacons, and network devices, while infrastructure-less networks rely on peer-to-peer WIT.
  • WIT zones exceed EH zones because information decoding requires much lower received power than energy harvesting.Typical information receivers operate from −130 dBm to −60 dBm, whereas EH devices usually need more than −30 dBm.
  • In-band WET offers better spectrum efficiency and simpler RF systems at EH devices, but dedicated WET can reduce spectrum efficiency and complicate design.Synchronization and successive interference cancellation can limit interference from deterministic WET signals.

C. WET-enabled Sustainable IoT

WET supports sustainable IoT by powering dense low-power deployments and extending device operation across applications including RFID, sensing, product labels, and wake-up radios.

  • 6G may support connectivity densities up to 10 devices/m3, making WET attractive for sustainably powering many low-cost, low-power IoT devices.The paper links WET with simplified maintenance, longer device durability, and reduced battery-waste processing.
  • 1) IoT WET Applications:: RFID, live labels, wireless sensor networks, and wake-up radios are identified as representative WET-powered IoT applications.
  • 1) IoT WET Applications:: At 1 m from a 3 W source, a building-monitoring sensor received 3.14, 2.88, 1.53, and 0.7 mW through air, wood, brick, and steel, respectively.The reported received power was sufficient for powering many state-of-the-art sensors.
  • 1) IoT WET Applications:: A passive RF wake-up radio can generate a wake-up pulse while keeping the sensor's active portion off during downtime.This reduces active listening and improves device lifetime.
  • Shared RF transmitters must accommodate potentially heterogeneous QoS requirements across different WET applications.Application-specific optimization can trade input power range, conversion efficiency, size, durability, complexity, and cost.

2) Green WET:

Green WET powers WET infrastructure from renewable sources through passive or active arrangements, while energy fluctuations and network-wide efficiency remain central design constraints.

  • 2) Green WET:: Green WET uses renewable energy to power power beacons or hybrid base stations, through passive distribution or active local harvesting.
  • 2) Green WET:: Active green WET is considered most attractive because local renewable sources can create a small-scale sustainable ecosystem.Its main constraint is the energy-availability variability typical of ambient harvesting.
  • 2) Green WET:: Hybrid combinations, intelligent energy balancing, and energy trading can improve robustness against shortages in green energy.
  • 2) Green WET:: Holistic optimization must account for short- and long-term fluctuations in energy availability to leverage green WET as a sustainable, efficient, and scalable technology.
  • Massive WET still faces challenges in end-to-end efficiency, network-wide WET-WIT integration, and powering many devices with ubiquitous access and QoS guarantees.

A. Energy Beamforming (EB)

Energy beamforming (EB) focuses energy through constructive superposition, but accurate CSI is costly for low-power devices. Distributed antenna systems (DAS), optimized placement, and hybrid DAS–EB deployments can improve coverage and fairness while reducing some CSI and deployment challenges.

  • Energy Beamforming (EB): EB weights signals across antennas to create constructive superposition and sharper beams, with the antenna count limiting the number of served beams.Serving S devices requires S ≤ M.
  • Energy Beamforming (EB): Accurate CSI is difficult to obtain because energy-harvesting devices may lack baseband processing, training consumes time and energy, and reliable feedback remains challenging.These costs can erase or reverse EB gains.
  • Distributed Antenna Systems (DAS): DAS reduces distance-related attenuation, helps eliminate blind spots, and homogenizes energy delivery across an area.Assigning smaller device groups to separate multi-antenna PBs can also alleviate CSI acquisition issues.
  • Distributed Antenna Systems (DAS): Optimized PB placement is essential: naive DAS can perform poorly, whereas location optimization yields significant gains but may be impractical for mobile devices and time-varying QoS.Deployment costs also rise with the number of PBs.
  • Distributed Antenna Systems (DAS): For a fixed total antenna count, multi-antenna PBs combining DAS and EB are preferable to a single multi-antenna PB, with more PBs favored as antenna count grows.The hybrid approach also reduces CSI acquisition overhead and associated energy expenditure relative to a single-PB implementation.
  • Distributed Antenna Systems (DAS): Radio stripes offer imperceptible, resilient distributed PB installations, but require optimized allocation, circuitry, prototypes, and low-signaling distributed processing.Their malleability can ease deployment-permission constraints.

1) Ultra-low-power Receivers:

Ultra-low-power receiver architectures and enhanced PB designs address the hardware and deployment constraints of massive WET. Candidate enhancements include integrated or flexible rectennas, radio stripes, rotatable PBs, UAV-based PBs, and reconfigurable antennas.

  • Ultra-low-power Receivers: Ultra-low-power receive architectures are essential because less stringent device energy demands make WET easier in large-scale IoT deployments.EH hardware optimization spans antennas, matching networks, rectifiers, and rectennas.
  • Ultra-low-power Receivers: Optically transparent glass antennas and flexible textile-integrated rectenna arrays support quasi-invisible or wearable energy-harvesting integration.These examples target seamless circuit integration into everyday objects and materials.
  • Enhanced PB: Radio stripe PBs enable imperceptible installation and potentially wide coverage, while massive antenna arrays and high-gain antennas improve transmitter hardware prospects.Rotor-equipped PBs with optimized rotation are reported as a possible enabler of local mWET.
  • Enhanced PB: Reconfigurable antennas and UAV swarms can adjust beam footprints and increase transmit gains, potentially improving QoS-based energy coverage of UAPBs.These mechanisms are presented as technological enablers for enhanced PB deployments.
  • Enhanced PB: IRS can opportunistically reconfigure the propagation environment without additional energy expenditure, supporting IRS-assisted WET scenarios.The architecture includes IRS elements between PBs and users, with control-channel requirements noted for some configurations.
  • Enhanced PB: IRS deployment has been shown in related RF-powered systems to allow important power saving at the hybrid BS/PB serving a QoS-constrained network.The cited analyses concern WPCN and SWIPT setups that the paper discusses as applicable to purely WET scenarios.

3) Reprogrammable Medium:

Reprogrammable propagation media and new spectrum bands can improve WET coverage, directionality, and path-loss compensation, but introduce hardware, CSI, alignment, safety, and scheduling challenges.

  • Reprogrammable Medium: IRS can compensate path loss through large apertures or provide alternative line-of-sight WET links around obstructed direct paths.These benefits are particularly relevant to reconfigurable propagation environments.
  • Reprogrammable Medium: High-precision IRS reflective elements may be expensive and difficult to scale because finer phase control requires more PIN diodes and control pins.Form-factor constraints further complicate the hardware design.
  • Reprogrammable Medium: Jointly designing IRS passive reflection and PB or hybrid-BS active beamforming creates difficult optimization problems, especially across frequency-selective sub-bands.The IRS passive beam design lacks the native frequency-selective adaptability of digital or hybrid active beamforming.
  • Reprogrammable Medium: In large low-power IoT deployments, the instantaneous CSI overhead needed to exploit IRS gains may be prohibitive, motivating CSI-limited or CSI-free schemes.Possible CSI acquisition methods include low-power IRS receive chains, known reflection patterns, or receiver feedback.
  • New Spectrum Opportunities: The 28−300 GHz spectrum offers large unused bandwidth, shorter-range directive propagation, and shorter wavelengths that support smaller devices or denser antenna arrays.These properties align with miniaturized, ultra-dense IoT deployments.
  • New Spectrum Opportunities: mmWave WET can exploit positioning under LOS conditions, but beam misalignment, non-LOS attenuation, rain, and possible human-safety concerns remain significant challenges.DAS and sensing-based human detection are identified as possible responses to coverage and safety issues.
  • Resource Scheduling and Optimization: WPCN scheduling must manage WET–WIT interference and optimize system objectives under time-varying channels and the causal link between current energy transfer and future transmission.Meta-distribution metrics are suggested for resource allocation with per-link service guarantees.
  • Resource Scheduling and Optimization: AI/ML can support adaptive, use-case-tailored reconfiguration, while on-chip intelligence remains constrained by IoT power limitations.The paper identifies smart wake-up and PMU functions as small-scale tasks for future on-chip intelligence.

F. Distributed Ledger Technology (DLT)

The paper identifies CSI acquisition, training overhead, and energy expenditure as major barriers to massive WET, while reviewing statistical-CSI approaches and DLT-related challenges for scalable energy services.

  • DLT-enabled energy services: DLT can support decentralized energy transactions, but large communication overhead, massive two-way connections, and energy-efficient protocol design remain challenges.Precise energy beamforming is also needed to meet agreed QoS for legitimate devices in energy-trading settings.
  • CSI acquisition: CSI acquisition becomes increasingly difficult as the number of EH devices grows, because training and collision avoidance consume scarce energy.More pilots may be needed within each coherence interval, making traditional training inefficient or unaffordable in low-power massive IoT deployments.
  • CSI-limited strategies: The reviewed approaches include a CSI-free scheme that uses device clustering information to reduce reliance on instantaneous CSI.The paper also considers partial or statistical CSI as an intermediate strategy for multi-antenna PBs.
  • Statistical CSI: Statistical CSI varies more slowly, requires less acquisition overhead and energy, and is less prone to estimation errors than instantaneous CSI.These properties are particularly relevant for massive-MIMO or LIS deployments with non-reciprocal channels.
  • Statistical CSI: An appropriate statistical precoder may reach near-optimum WET performance, especially when strong line-of-sight propagation is available.In the illustrated 32-antenna, 10 m-radius scenario, performance improves as the Rician LOS factor increases; second-order statistics may improve it further but require more channel sampling.

B. CSI-free Schemes

CSI-free WET schemes are examined for ultra-low-power and massive deployments, with performance depending on energy requirements, spatial uniformity, hardware complexity, and available positioning information.

  • Scheme comparison: CSI-free WET schemes differ in spatial behavior: APS-EMW, SA, and AA-IS provide uniform performance, while AA-SSmin var and AA-SSmax E favor particular directions.The comparison uses a four-antenna PB at the center of a 10 m × 10 m area, with harvester sensitivity of −22 dBm, saturation of −8 dBm, and 35% conversion efficiency.
  • Figures: The figure comparison covers APS-EMW, AA-IS, SA, AA-SSmin var, and AA-SSmax E using heatmaps of average harvested energy in dBm.The corresponding coverage figure reports area coverage for different average harvested-energy requirements.
  • Performance and coverage: Coverage at the stated average harvested-energy threshold is 9% for the baseline and rises to 13% with APS-EMW, SA, or AA-IS, and 18% with AA-SSmax E.These values describe area coverage under the CSI-free schemes in the illustrated setup.
  • Performance and coverage: APS-EMW is preferable for massive EH deployments with ultra-low energy demands, whereas other CSI-free schemes generally perform better under stricter requirements.SA is the only discussed scheme requiring a single RF chain for optimum operation, reducing circuit power, hardware complexity, and operational costs.
  • Positioning-based schemes: Positioning information can improve CSI-free WET by enabling antenna-array rotation, LOS geometric-MIMO beam design, and cluster-aware powering.The proposed design considerations include angular-domain clustering, per-cluster power or time allocation, and antenna selection based on angular dispersion.
  • Positioning-based schemes: The example positioning-based setup divides the IoT deployment into clusters containing 4, 8, and 15 EH devices while accounting for positioning-accuracy margins.The inner and outer triangles represent predictable device regions and regions expanded by positioning uncertainty, respectively.

D. Hybrid Schemes

Hybrid WET schemes are needed for heterogeneous deployments combining devices with different CSI and positioning capabilities, while CSI-free powering can bootstrap more advanced energy-transfer protocols.

  • Hybrid architectures: Heterogeneous networks may require hybrid schemes combining full-CSI, CSI-limited, CSI-free, and positioning-based energy beamforming.Designs must account for finite pilot pools and all available side information.
  • Bootstrapped powering: CSI-free WET can initially energize severely depleted devices before they acquire CSI or positioning information for subsequent high-gain powering.The paper leaves the frequency and duration of this initial CSI-free stage, together with its protocols, as open research questions.
  • Conclusions: Average CSI can attain near-optimum performance with limited overhead, while the paper identifies CSI acquisition as a key limitation for massive WET.The conclusions also review DAS, EB, hardware, programmable-medium, spectrum, scheduling, and DLT enablers and identify further research directions.
  • Conclusions: The paper summarizes its identified challenges and research directions in Table V.
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