… our deep channel estimator lies in not requiring any statistical knowledge about the channel
… know or learn the channel statistics, but also makes the estimator applicable to any kind of …

… Furthermore, compared with minimum mean square error, there are no need for channel …
In summary, deep neural network is an efficient method for the imperfect channel estimation …

… of the channel and noise (complex to implement in practice). We present a deep learning
framework based on deep neural networks (DNNs) for 5G MaMIMO channel estimation. After a …

… In order to focus on channel estimation, we did not add forward error correction coding
technology. By applying a suitable direct current (DC) to x ( n ) , non-negative signals are …

… -OFDM systems, since complicated multipath channels may cause a serious decline in …
estimation. To address the issue, two types of channel estimators based on deep neural networks …

… channel estimator are jointly designed by using both a twolayer neural network (TNN) and a
deep neural network (… -based channel estimation scheme by using the neural networks. The …

… A two-phase approach is presented to estimate the channel grid. First, an image super… IR
networks, we have used two recently developed CNN-based (Convolutional Neural Network) …

… twolayer neural networks (TNNs) and a channel estimator using deep … neural networks
(TNNs) for pilot design and we also construct deep neural networks (DNNs) for channel estimation…

… use of deep neural network (DNN) to develop a decision-directed (DD)-channel estimation
(… We propose the use of DNN for k-step channel prediction for space-time block code (STBC), …

… TT-DNN based channel estimator outperforms the … estimation accuracy, and robustness,
and presents better performance than recurrent neural network for semiblind channel estimation…