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- W4285697901 abstract "In this paper, we present a downlink pilot design scheme for Deep Learning (DL) based channel estimation (ChannelNet) in orthogonal frequency-division multiplexing (OFDM) systems. Specifically, in the proposed scheme, a feature selection method named Concrete Autoencoder (ConcreteAE) is used to find the most informative locations for pilot transmission. This autoencoder consists of a concrete layer as the encoder and a multilayer perceptron (MLP) as the decoder. During the training, the concrete layer selects the most informative pilot locations, and the decoder reconstructs an approximate estimation of the channel. Eventually, the ChannelNet is trained on the output of the ConcreteAE aiming to reconstruct the ideal channel response. The estimation error results show that this approach outperforms the previously presented ChannelNet with a uniformly distributed pilot pattern, and its performance is comparable to the minimum mean square error (MMSE)." @default.
- W4285697901 created "2022-07-18" @default.
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- W4285697901 date "2020-03-19" @default.
- W4285697901 modified "2023-10-14" @default.
- W4285697901 title "Pilot Pattern Design for Deep Learning-Based Channel Estimation in OFDM Systems" @default.
- W4285697901 doi "https://doi.org/10.48550/arxiv.2003.08980" @default.
- W4285697901 hasPublicationYear "2020" @default.
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