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AuthorOmri, Aymen
AuthorHamila, R.
AuthorHasna, M.
AuthorBouallegue, R.
AuthorChaieb, H.
Available date2022-03-17T05:31:19Z
Publication Date2010
Publication NameQatar Foundation Annual Research Forum Proceedings
Resourceqscience
CitationOmri A et al. (2010). Estimation of highly selective channels for downlink LTE systemby a robust neural network. Qatar Foundation Annual Research Forum Proceedings 2010: CSP6 https://doi.org/10.5339/qfarf.2010.CSP6.
ISSN2226-9649
URIhttps://doi.org/10.5339/qfarf.2010.CSP6
URIhttp://hdl.handle.net/10576/28245
AbstractIn this paper we propose a robust channel estimator for the downlink of a Long Term Evolution (LTE) system using a highly selective neural network. This method uses the information provided by the reference signals to estimate the total frequency response of the channel in two phases. In the first phase, the proposed method learns to adapt to the channel variations, and in the second phase it predicts the channel parameters. The performance of the estimation method in terms of complexity and quality is confirmed by theoretical analysis and simulations in an LTE/OFDMA (Orthogonal Frequency-Division Multiple Access) transmission system. The performance of the proposed channel estimator is compared with those of least-square decision feedback and modified Wiener methods. The simulation results show that the proposed estimator performs better than the above estimators and it is more robust at high-speed mobility.
Languageen
PublisherHamad bin Khalifa University Press (HBKU Press)
SubjectLong Term Evolution (LTE) system
neural network
LTE/OFDMA (Orthogonal Frequency-Division Multiple Access)
TitleEstimation of highly selective channels for downlink LTE systemby a robust neural network
TypeConference Paper
Issue Number1
Volume Number2010


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