1,964 research outputs found

    Integrated cmos rectifier for rf-powered wireless sensor network nodes

    Get PDF
    This article presents a review of the CMOS rectifier for radio frequency energy harvesting application. The on-chip rectifier converts the ambient low-power radio frequency signal coming to antenna to useable DC voltage that recharges energy to wireless sensor network (WSN) nodes and radiofrequency identification (RFID) tags, therefore the rectifier is the most important part of the radio frequency energy harvesting system. The impedance matching network maximizes power transfer from antenna to rectifier. The design and comparison between the simulation results of one- and multi-stage differential drive cross connected rectifier (DDCCR) at the operating frequencies of 2.44GHz, and 28GHz show the output voltage of the multi-stage rectifier doubles at each added stage and power conversion efficiency (PCE) of rectifier at 2.44GHz was higher than 28GHz. The (DDCCR) rectifier is the most efficient rectifier topology to date and is used widely for passive WSN nodes and RFID tags

    Radio Frequency Energy Harvesting and Management for Wireless Sensor Networks

    Full text link
    Radio Frequency (RF) Energy Harvesting holds a promising future for generating a small amount of electrical power to drive partial circuits in wirelessly communicating electronics devices. Reducing power consumption has become a major challenge in wireless sensor networks. As a vital factor affecting system cost and lifetime, energy consumption in wireless sensor networks is an emerging and active research area. This chapter presents a practical approach for RF Energy harvesting and management of the harvested and available energy for wireless sensor networks using the Improved Energy Efficient Ant Based Routing Algorithm (IEEABR) as our proposed algorithm. The chapter looks at measurement of the RF power density, calculation of the received power, storage of the harvested power, and management of the power in wireless sensor networks. The routing uses IEEABR technique for energy management. Practical and real-time implementations of the RF Energy using Powercast harvesters and simulations using the energy model of our Libelium Waspmote to verify the approach were performed. The chapter concludes with performance analysis of the harvested energy, comparison of IEEABR and other traditional energy management techniques, while also looking at open research areas of energy harvesting and management for wireless sensor networks.Comment: 40 pages, 9 figures, 5 tables, Book chapte

    On Spectrum Sharing Between Energy Harvesting Cognitive Radio Users and Primary Users

    Full text link
    This paper investigates the maximum secondary throughput for a rechargeable secondary user (SU) sharing the spectrum with a primary user (PU) plugged to a reliable power supply. The SU maintains a finite energy queue and harvests energy from natural resources and primary radio frequency (RF) transmissions. We propose a power allocation policy at the PU and analyze its effect on the throughput of both the PU and SU. Furthermore, we study the impact of the bursty arrivals at the PU on the energy harvested by the SU from RF transmissions. Moreover, we investigate the impact of the rate of energy harvesting from natural resources on the SU throughput. We assume fading channels and compute exact closed-form expressions for the energy harvested by the SU under fading. Results reveal that the proposed power allocation policy along with the implemented RF energy harvesting at the SU enhance the throughput of both primary and secondary links

    Max-min Fair Beamforming for SWIPT Systems with Non-linear EH Model

    Full text link
    We study the beamforming design for multiuser systems with simultaneous wireless information and power transfer (SWIPT). Employing a practical non-linear energy harvesting (EH) model, the design is formulated as a non-convex optimization problem for the maximization of the minimum harvested power across several energy harvesting receivers. The proposed problem formulation takes into account imperfect channel state information (CSI) and a minimum required signal-to-interference-plus-noise ratio (SINR). The globally optimal solution of the design problem is obtained via the semidefinite programming (SDP) relaxation approach. Interestingly, we can show that at most one dedicated energy beam is needed to achieve optimality. Numerical results demonstrate that with the proposed design a significant performance gain and improved fairness can be provided to the users compared to two baseline schemes.Comment: Invited paper, IEEE VTC 2017, Fall, Toronto, Canad
    • …
    corecore