Hybrid evolutionary-based sparse channel estimation for IRS-assisted mmWave MIMO systems
The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication
system has emerged as a promising technology for coverage extension and capacity …
system has emerged as a promising technology for coverage extension and capacity …
Deep learning-based CSI feedback for beamforming in single-and multi-cell massive MIMO systems
The potentials of massive multiple-input multiple-output (MIMO) are all based on the
available instantaneous channel state information (CSI) at the base station (BS). Therefore …
available instantaneous channel state information (CSI) at the base station (BS). Therefore …
Joint Bayesian channel estimation and data detection for OTFS systems in LEO satellite communications
Lower earth orbit (LEO) satellites play an important role in the integration of space and
terrestrial communication networks, which typically encounter high-mobility scenarios. It has …
terrestrial communication networks, which typically encounter high-mobility scenarios. It has …
A block sparsity based estimator for mmWave massive MIMO channels with beam squint
Multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication is a key
technology for next generation wireless networks. One of the consequences of utilizing a …
technology for next generation wireless networks. One of the consequences of utilizing a …
Robust deep learning for uplink channel estimation in cellular network under inter-cell interference
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 …
most existing works focus on the white Gaussian noise which are inapplicable for cell-edge …
Group-sparsity learning approach for bearing fault diagnosis
Fault impulse extraction under strong background noise and/or multiple interferences is a
challenging task for bearing fault diagnosis. Sparse representation has been widely applied …
challenging task for bearing fault diagnosis. Sparse representation has been widely applied …
Robust channel estimation for RIS-aided millimeter-wave system with RIS blockage
Recently, the reconfigurable intelligent surface (RIS) aided communication system has
emerged as a promising candidate for future wireless communications. The existing channel …
emerged as a promising candidate for future wireless communications. The existing channel …
Block-sparse signal recovery via general total variation regularized sparse Bayesian learning
One of the main challenges in block-sparse signal recovery, as encountered in, eg, multi-
antenna mmWave channel models, is block-patterned estimation without knowledge of …
antenna mmWave channel models, is block-patterned estimation without knowledge of …
Sparse reconstruction using block sparse Bayesian learning with fast marginalized likelihood maximization for near-infrared spectroscopy
T Pan, C Wu, Q Chen - IEEE Transactions on Instrumentation …, 2021 - ieeexplore.ieee.org
The absorption peak contains a great amount of important chemical information that is
critical for the qualitative/quantitative analysis of organic compounds in high-dimensional …
critical for the qualitative/quantitative analysis of organic compounds in high-dimensional …
Sparse Bayesian learning approach for baseline correction
Spectral techniques in analytical chemistry are often affected by baselines in practical
implementation. Without baseline correction, the accuracy of the qualitative/quantitative …
implementation. Without baseline correction, the accuracy of the qualitative/quantitative …