Neural Network based Channel Identification and Compensation

Omura, Takaki; Kojima, Satoko; Maruta, Kazuki; Ahn, Chang-Jun

doi:10.1109/iscit.2018.8587981

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2019

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Cited by 2 publications

(2 citation statements)

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“…To improve the accuracy of estimated CSI transition, we proposed an artificial neural network (ANN) approach [3]. The proposed method first applies data-aided CSI estimation for some data symbols and the trains a neural network.…”

Section: Introductionmentioning

confidence: 99%

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Neural network based channel identification and compensation

et al. 2019

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This letter proposes a neural network based channel identification and compensation methods for an OFDM system. Under the fast fading environment, pilot-aided channel estimation suffers from channel state fluctuation particularly in the last part of the packet. The proposed approach can estimate the whole transition of channel states and efficiently compensate the channel variation using the generalization capability of a neural network. The computer simulation results clarify its effectiveness via improved BER performance even under the stringent Doppler shift.

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Section: Introductionmentioning

confidence: 99%

“…The generalization capability of a neural network interpolates the whole transition of channel state and compensates the channel variation. This letter refined overall evaluation with employing more simplified ANN algorithms than our previous study [3].…”

Section: Introductionmentioning

confidence: 99%