MATEC Web Conf.
Volume 189, 20182018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018)
|Number of page(s)||9|
|Published online||10 August 2018|
Deep neural network for orthogonal frequency division multiplexing systems without cyclic prefix transmission
Electronic Engineering Department, Myongji University, Yongin, 449-728, Korea
Corresponding author : firstname.lastname@example.org
Orthogonal frequency division multiplexing (OFDM) is widely used in wired or wireless transmission systems. In the structure of OFDM, a cycle prefix (CP) has been exploited to avoid the effects of inter-symbol interference (ISI) and inter-carrier interference (ICI). This paper proposes a new approach to transmit the signals without CP transmission. Using the deep neural network, the proposed OFDM system transmits data without the CP. Simulation results show that the proposed scheme can estimate the CP at the receiver and overcome the effect of ISI.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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