Wirelessly-Powered Sensor Networks: Power Allocation for Channel Estimation and Energy Beamforming

Du, Rong; Shokri‐Ghadikolaei, Hossein; Fischione, Carlo

doi:10.1109/twc.2020.2969659

Cited by 18 publications

(22 citation statements)

References 46 publications

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“…The amount of energy harvested by edge devices only depends on the transmission power θ and the channel power gain h ji (∀j ∈ N , i ∈ M). We employ a typical linear energy harvesting model like previous works [41]. Therefore, the harvested energy by edge device i from WPT nodes could be shown as:…”

Section: B Wirelessly-powered Computing Model For Learningmentioning

confidence: 99%

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Lin

Bashir

et al. 2022

IEEE Internet Things J.

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Recently, edge artificial intelligence techniques (e.g., federated edge learning) are emerged to unleash the potential of big data from Internet of Things (IoT). By learning knowledge on local devices, data privacy-preserving and quality of service (QoS) are guaranteed. Nevertheless, the dilemma between the limited on-device battery capacities and the high energy demands in learning is not resolved. When the on-device battery is exhausted, the edge learning process will have to be interrupted. In this paper, we propose a novel Wirelessly Powered Edge intelliGence (WPEG) framework, which aims to achieve a stable, robust, and sustainable edge intelligence by energy harvesting (EH) methods. Firstly, we build a permissioned edge blockchain to secure the peer-to-peer (P2P) energy and knowledge sharing in our framework. To maximize edge intelligence efficiency, we then investigate the wirelessly-powered multi-agent edge learning model and design the optimal edge learning strategy. Moreover, by constructing a two-stage Stackelberg game, the underlying energy-knowledge trading incentive mechanisms are also proposed with the optimal economic incentives and power transmission strategies. Finally, simulation results show that our incentive strategies could optimize the utilities of both parties compared with classic schemes, and our optimal learning design could realize the optimal learning efficiency.

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Section: B Wirelessly-powered Computing Model For Learningmentioning

confidence: 99%

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Lin

Bashir

et al. 2022

IEEE Internet Things J.

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“…However, most of the works so far are concerned with rather optimistic setups where either i) most of the power consumption sources at the EH devices are ignored, ii) Channel State Information (CSI) procedures are assumed cost free, and iii) only one or few EH devices are powered. Regarding the latter, the number of EH devices is often not greater than the number of powering antennas such that full gain from energy beamforming (EB) is attained in the WET phase, e.g., [12], [17], [18]. For instance, a setup where a multi-antenna hybrid access point (HAP) transfers power to the devices via EB, followed by the devices sending their data simultaneously by consuming the harvested energy, is investigated in [17].…”

Section: A Related Workmentioning

confidence: 99%

“…However, no other power consumption sources besides transmissions are considered, and Zero Forcing (ZF) equalization is used for information decoding at the HAP without analysing the CSI acquisition costs. Meanwhile, the authors in [18] do consider the CSI acquisition costs when optimizing the HAP pilots power and the power allocated to the energy transmission, while the EH devices are under the effect of several power consumption sources. Yet, the imposition of having more antennas than devices may be strong towards future low-power massive IoT networks.…”

Section: A Related Workmentioning

confidence: 99%

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CSI-Free vs CSI-Based Multi-Antenna WET for Massive Low-Power Internet of Things

López

Mahmood

Alves

2021

IEEE Trans. Wireless Commun.

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Wireless Energy Transfer (WET) is a promising solution for powering massive Internet of Things deployments. An important question is whether the costly Channel State Information (CSI) acquisition procedure is necessary for optimum performance. In this paper, we shed some light into this matter by evaluating CSI-based and CSI-free multi-antenna WET schemes in a setup with WET in the downlink, and periodic or Poisson-traffic Wireless Information Transfer (WIT) in the uplink. When CSI is available, we show that a maximum ratio transmission beamformer is close to optimum whenever the farthest node experiences at least 3 dB of power attenuation more than the remaining devices. On the other hand, although the adopted CSI-free mechanism is not capable of providing average harvesting gains, it does provide greater WET/WIT diversity with lower energy requirements when compared with the CSIbased scheme. Our numerical results evidence that the CSI-free scheme performs favorably under periodic traffic conditions, but it may be deficient in case of Poisson traffic, specially if the setup is not optimally configured. Finally, we show the prominent performance results when the uplink transmissions are periodic, while highlighting the need of a minimum mean square error equalizer rather than zero-forcing for information decoding.

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“…For each time slot following the group of mini-slots in a frame, a base station selects a device to send RF energy, and the device attempts to harvest the RF energy. In [39], a base station uses energy beamforming to directionally send wireless energy to a device in a time slot, to another device in the next slot, and so on; such an energy transfer scheme has good performance with lower complexity. This paper employs the technology that a base station sends wireless energy directionally to a device in a slot time; thus, it is assumed that an The mini-slot is used to merely send a device's necessary information to the base station.…”

Section: Structures Of Time Slots and Framesmentioning

confidence: 99%

A Framed Slotted ALOHA-Based MAC for Eliminating Vain Wireless Power Transfer in Wireless Powered IoT Networks

Lin

Tzeng

2020

Electronics

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Multiple access control (MAC) is crucial for devices to send data packets and harvest wireless energy in wireless powered Internet of Things (IoT) networks. A framed slotted ALOHA (FSA) protocol is employed in several practical networks. This paper studies an FSA-based MAC in a centralized wireless powered IoT network, including half-duplex devices and a full-duplex base station transmitting wireless energy in an intended direction. Under such a network, it is possible that a half-duplex device contends for a time slot to transmit a packet while the base station transmits wireless energy to the device in the same time slot, which causes vain charging and wastes the opportunity to charge other devices. To eliminate the vain charging, this paper designs a MAC in which a base station utilizes the information conveyed from devices in advance to arrange the charging order of devices. The novelty is to develop an algorithm to find a charging order of half-duplex devices instead of using full-duplex devices to eliminate the vain charging. Event-driven simulations are conducted to study the performance of the proposed MAC. Simulation results show that the proposed MAC produces better system performances than the system not eliminating the vain charging. In summary, the application of the proposed MAC yields the benefits of higher throughput and lower packet loss.

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Wirelessly-Powered Sensor Networks: Power Allocation for Channel Estimation and Energy Beamforming

Cited by 18 publications

References 46 publications

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

CSI-Free vs CSI-Based Multi-Antenna WET for Massive Low-Power Internet of Things

A Framed Slotted ALOHA-Based MAC for Eliminating Vain Wireless Power Transfer in Wireless Powered IoT Networks

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