… For the OMP-based channel estimation scheme, we consider that the sparsity level of the
beamspace channel vector is S = 24. For the AMP-based channel estimation scheme, we set …

… the LS-based channel estimation value as the network input and exploits a neural network to
further improve the estimation … Jin, and GY Li, “Deep learning-based channel estimation for …

… In this paper, we propose a new DNN based channel estima… Pan, and GY Li, “Deep learning
based channel estimation for … Jin, and GY Li, “Deep learning–based channel estimation for …

… used for the downlink channel estimation. Since the BS … channel estimation where the
neural network (NN) is trained and utilized for prediction at the BS. For uplink channel estimation, …

… prediction, AI methods are also used to tackle classical channel estimation problem, such
as [77], [79]. The novelty of the predictor proposed in this article is mainly that it makes use of …

… proposes a deep denoising neural network assisted compressive channel estimation for …
Jin, and GY Li, “Deep learning-based channel estimation for beamspace mmWave massive …

… channel agnostic end-to-end learning based approach with the traditional methods, which
are designed based on the channel … Jin, and GY Li, “Deep learning-based channel estimation …

… Abstract—Machine learning (ML) has … neural network (CNN) trained on the local datasets
of the users without sending them to the BS. We develop FL-based channel estimation …

… the explicit channel estimation phase altogether, a machine learning approach can achieve
a significantly higher transmission rate than the conventional channel estimation based …

… channel estimation error and data-aided channel estimation.… Conventional pilot-only based
channel estimation is performed … estimator as additional pilot symbols to refine the channel …