This paper proposes a novel tensor-train deep neural network (TT-DNN) based channel
estimator to tackle challenges of time-varying channel estimation in multiple-input multiple …

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

Channel estimation with received signal-to-noise ratio (SNR) feedback is promising and
effective for practical wireless multiple-input multiple-output (MIMO) systems. In this paper …

For estimating the MIMO channels for both the uplink and the downlink, we used residual
deep neural network. The proposed residual deep neural network (ResNET) channel …

To overcome the influence of channel estimation error on signal detection, this paper
presents a model-driven deep learning method for joint channel estimation and signal …

We consider the use of deep neural network (DNN) to develop a decision-directed (DD)-
channel estimation (CE) algorithm for multiple-input multiple-output (MIMO)-space-time …

Recently, convolutional neural network (CNN)-based channel estimation (CE) for massive
multiple-input multiple-output communication systems has achieved remarkable success …

This paper is concerned with channel estimation in MIMO systems with few-bit ADCs. In
these systems, a linear minimum mean-squared error (MMSE) channel estimator obtained in …

Channel estimation plays a critical role in the system performance of wireless networks. In
addition, deep learning has demonstrated significant improvements in enhancing the …

As future wireless systems trend towards higher carrier frequencies and large antenna
arrays, receivers with one-bit analog-to-digital converters (ADCs) are being explored owing …