Aiming at the problem that the downlink channel estimation performance is limited due to the
fast time-varying and non-stationary characteristics in the high-speed mobile scenarios, we …

Deep learning (DL) has emerged as an effective tool for channel estimation in wireless
communication systems, especially under some imperfect environments. However, even …

Recently, in order to achieve performance improvement in scenarios where the channel is
either unknown, or too complex for an analytical description, Neural Network (NN) based …

The research about deep learning application for physical layer has been received much
attention in recent years. In this paper, we propose a Deep Learning (DL) based channel …

Improving the accuracy of channel estimation is a significant topic in the context of wireless
communications. For training-based channel estimations, increasing the length of a training …

In this paper, online deep learning (DL)-based channel estimation algorithm for doubly
selective fading channels is proposed by employing the deep neural network (DNN). With …

Deep learning has demonstrated the important roles in improving the system performance
and reducing computational complexity for 5G-and-beyond networks. In this paper, we …

Deep learning (DL)-based channel estimation has achieved remarkable success. However,
most existing works focus on the white Gaussian noise which are inapplicable for cell-edge …

Channel state information (CSI) plays a vital role in adaptive transmission systems, which
adapt their transmission parameters to instantaneous channel conditions. However, the CSI …

The combination of non-orthogonal multiple access (NOMA) and reconfigurable intelligent
surface (RIS) technologies is proposed to meet the demands of data rate, latency, and …