Channel estimation methods for diversity and MIMO in LR-WPAN systems

Huhn, Alexander; Schwetlick, Horst

doi:10.1109/icce.2010.5418961

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…However, the coherent addition of the signals transmitted from the ET at the ER depends on the availability of channel state information (CSI), which necessitates channel estimation. The estimation process involves analog to digital conversion and baseline processing, which require significant energy [11,12]. Under tight energy constraints and hardware limitations, such an estimation process may become infeasible at the ER.…”

Section: Introductionmentioning

confidence: 99%

Wireless Energy Beamforming Using Received Signal Strength Indicator Feedback

Abeywickrama

Samarasinghe

et al. 2018

IEEE Trans. Signal Process.

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Multiple antenna techniques that allow energy beamforming have been looked upon as a possible candidate for increasing the transfer efficiency between the energy transmitter (ET) and the energy receiver (ER) in wireless power transfer. This paper introduces a novel scheme that facilitates energy beamforming by utilizing Received Signal Strength Indicator (RSSI) values to estimate the channel. Firstly, in the training stage, the ET will transmit using each beamforming vector in a codebook, which is pre-defined using a Cramer-Rao lower bound analysis. RSSI value corresponding to each beamforming vector is fed back to the ET, and these values are used to estimate the channel through a maximum likelihood analysis. The results that are obtained are remarkably simple, requires minimal processing, and can be easily implemented. The paper also validates the analytical results numerically, as well as experimentally, and it is shown that the proposed method achieves impressive results.Index Terms-Wireless energy transfer, energy beamforming, received signal strength indicator (RSSI), Cramer-Rao lower bound, channel learning.

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Section: Introductionmentioning

confidence: 99%

Wireless Energy Beamforming Using Received Signal Strength Indicator Feedback

Abeywickrama

Samarasinghe

et al. 2018

IEEE Trans. Signal Process.

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“…However, the coherent addition of the signals transmitted from the ET at the ER depends on the availability of channel state information (CSI), which necessitates channel estimation at the ER in most cases. The estimation process involves analog to digital conversion and baseline processing which require significant energy [4,5]. Under tight energy constraints and hardware limitations, such an estimation process may become infeasible at the ER.…”

Section: Introductionmentioning

confidence: 99%

Wireless Energy Beamforming Using Signal Strength Feedback

Abeywickrama

Samarasinghe

2016

2016 IEEE Global Communications Conference (GLOBECOM)

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Abstract-Multiple antenna techniques, that allow energy beamforming, have been looked upon as a possible candidate for increasing the efficiency of the transfer process between the energy transmitter (ET) and the energy receiver (ER) in wireless energy transfer. This paper introduces a novel scheme that facilitates energy beamforming by utilizing Received Signal Strength Indicator (RSSI) values to estimate the channel. Firstly, in the training stage, the ET will transmit sequentially using each beamforming vector in a codebook, which is pre-defined using a Cramer-Rao lower bound analysis. The RSSI value corresponding to each beamforming vector is fed back to the ET, and these values are used to estimate the channel through a maximum likelihood analysis. The results that are obtained are remarkably simple, requires minimal processing, and can be easily implemented. Also, the results are general and hold for all well known fading models. The paper also validates the analytical results numerically, as well as experimentally, and it is shown that the proposed method achieves impressive results in wireless energy transfer. I. INTRODUCTIONWireless energy transfer (WET) focuses on delivering energy to charge freely located devices, over the air interface, using Electromagnetic radiation in the radio frequency (RF) bands [2]. When it comes to RF signal enabled WET, increasing the efficiency of the energy transfer between the energy transmitter (ET) and the energy receiver (ER) is of paramount importance. Multiple antenna techniques that also enhance the range between the ET and the ERs have been looked upon as a possible solution to address this concern [3]. This paper proposes a novel approach that increases the efficiency of a WET system that utilizes multiple antennas to facilitate the energy transfer.To this end, multiple antennas at the ET enable focusing the transmitted energy to the ERs via beamforming. However, the coherent addition of the signals transmitted from the ET at the ER depends on the availability of channel state information (CSI), which necessitates channel estimation at the ER in most cases. The estimation process involves analog to digital conversion and baseline processing which require significant energy [4,5]. Under tight energy constraints and hardware limitations, such an estimation process may become infeasible at the ER. In this paper, we propose a more energy efficient

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Channel estimation methods for diversity and MIMO in LR-WPAN systems

Cited by 2 publications

References 2 publications

Wireless Energy Beamforming Using Received Signal Strength Indicator Feedback

Wireless Energy Beamforming Using Received Signal Strength Indicator Feedback

Wireless Energy Beamforming Using Signal Strength Feedback

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