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Channel Estimation Based on Neural Network With Feedback for Mimo Ofdm Mobile Communication Systems


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Abstract

multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) has received a great deal of attention of recently in achieving high data rate in wireless communication systems such as WIMAX. Channel estimation is, however, a critical issue for coherent demodulation. In this paper, a new channel estimator based on neural network with feedback for MIMO-OFDM mobile system is designed and its performance is compared to the least square error (LS), least mean square error (LMS), minimum mean square error (MMSE) algorithms and neural network without feedback by using computer simulations. Simulation results demonstrate that our proposed system is an effective solution to channel estimation in time varying fast fading channels without any knowledge of channel statistics and noise information.


Keywords


Pages

Total Pages: 10
Pages: 307-316

DOI
10.1080/10798587.2008.10643245


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Published

Volume: 18
Issue: 3
Year: 2012

Cite this document


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JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

TWO YEAR CITATIONS PER DOCUMENT (SJR DATA): 0.993 (2018)
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."





Journal: 1995-Present


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