The Experts below are selected from a list of 222 Experts worldwide ranked by ideXlab platform
Xianbin Wang - One of the best experts on this subject based on the ideXlab platform.
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Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction and Restoration
arXiv: Information Theory, 2019Co-Authors: Muye Li, Shun Zhang, Nan Zhao, Weile Zhang, Xianbin WangAbstract:On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-Grid refinement. Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-Grid Bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-Grid Bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.
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Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction, and Restoration
IEEE Transactions on Communications, 2019Co-Authors: Muye Li, Shun Zhang, Nan Zhao, Weile Zhang, Xianbin WangAbstract:To estimate time-varying MIMO channel at base station, traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to extremely high overhead in massive MIMO systems. To tackle this problem, we propose a novel scheme for DL channel tracking in this paper. First, by utilizing virtual channel representation (VCR), we develop a dynamic uplink (UL) massive MIMO channel model with the consideration of off-Grid refinement. Then, a coordinate-wise expectation maximization (EM) algorithm is adopted for capturing model parameters, including the spatial signatures, time-correlation factors, off-Grid Bias, channel power, and noise power. By exploiting the UL/DL angle reciprocity, the spatial signatures, time-correlation factors and off-Grid Bias of the DL channel model can be reconstructed with the knowledge of UL. However, channel power and noise power are closely related with the carrier frequency, which cannot be perfectly inferred from the UL. Instead of discovering these two parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with partial prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.
Muye Li - One of the best experts on this subject based on the ideXlab platform.
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CSPS - Massive MIMO Channel Estimation via Generalized Approximate Message Passing.
Lecture Notes in Electrical Engineering, 2020Co-Authors: Muye Li, Weile Zhang, Shun ZhangAbstract:In this paper, we proposed a channel estimation scheme for an off-Grid massive MIMO channel model, with the consideration of carrier frequency offset at the BS antenna array. We first developed an off-Grid channel model for the spatial sample mismatching problem. Then, an EM based sparse Bayesian learning framework was built to capture the model parameters, i.e., the off-Grid Bias and the CFO. While in the learning process, a damped generalized approximate message passing algorithm was introduced to obtain accurate needed posterior statistics. Finally, simulation results are exhibited to certify the performance of our proposed scheme.
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Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction and Restoration
arXiv: Information Theory, 2019Co-Authors: Muye Li, Shun Zhang, Nan Zhao, Weile Zhang, Xianbin WangAbstract:On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-Grid refinement. Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-Grid Bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-Grid Bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.
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Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction, and Restoration
IEEE Transactions on Communications, 2019Co-Authors: Muye Li, Shun Zhang, Nan Zhao, Weile Zhang, Xianbin WangAbstract:To estimate time-varying MIMO channel at base station, traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to extremely high overhead in massive MIMO systems. To tackle this problem, we propose a novel scheme for DL channel tracking in this paper. First, by utilizing virtual channel representation (VCR), we develop a dynamic uplink (UL) massive MIMO channel model with the consideration of off-Grid refinement. Then, a coordinate-wise expectation maximization (EM) algorithm is adopted for capturing model parameters, including the spatial signatures, time-correlation factors, off-Grid Bias, channel power, and noise power. By exploiting the UL/DL angle reciprocity, the spatial signatures, time-correlation factors and off-Grid Bias of the DL channel model can be reconstructed with the knowledge of UL. However, channel power and noise power are closely related with the carrier frequency, which cannot be perfectly inferred from the UL. Instead of discovering these two parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with partial prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.
Hongyoung Chang - One of the best experts on this subject based on the ideXlab platform.
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effects of substrate Bias voltage on plasma parameters in temperature control using a Grid system
Physics of Plasmas, 2001Co-Authors: Ji Hong, Hongyoung ChangAbstract:In this paper we investigate the effects of substrate Bias voltage on plasma parameters in temperature control using a Grid system in inductively coupled plasma. Electron temperature can be controlled from 2.5 eV to 0.5 eV at 1 mTorr Ar plasma using Grid Bias voltage, and the electron temperature is a strong function of substrate Bias voltage. The main control parameter determining the electron temperature is the potential difference between Grid-Biased voltage and the plasma potential in the temperature controlled region (ΔφII,g). When substrate Bias voltage is negative, plasma parameters do not vary with substrate Bias voltage due to constant ΔφII,g
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electron temperature control with Grid Bias in inductively coupled argon plasma
Physics of Plasmas, 1999Co-Authors: Jungin Hong, N S Yoon, Choongseock Chang, Hongyoung ChangAbstract:The mechanism of controlling electron temperature with Grid-Biased voltage is studied experimentally and the relevant physics is discussed in an inductively coupled Ar discharge. To obtain the electron density and electron temperature, the electron energy distribution functions (EEDFs) are measured with a Langmuir probe. As the Grid voltage decreases negatively, the effective electron temperature is controlled from 2.0 to 0.6 eV and the electron density changes from 3×1010 to 2×1010 cm−3 in the diffusion region, while the effective electron temperature and electron density are not changed in the source region. The dependence of such various parameters, as electron density, electron temperature, plasma potential in each region, and so on, on the applied voltage, is presented. The functional relations between the measured physical quantities are well explained based on a global particle and energy balance relations.
Shun Zhang - One of the best experts on this subject based on the ideXlab platform.
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CSPS - Massive MIMO Channel Estimation via Generalized Approximate Message Passing.
Lecture Notes in Electrical Engineering, 2020Co-Authors: Muye Li, Weile Zhang, Shun ZhangAbstract:In this paper, we proposed a channel estimation scheme for an off-Grid massive MIMO channel model, with the consideration of carrier frequency offset at the BS antenna array. We first developed an off-Grid channel model for the spatial sample mismatching problem. Then, an EM based sparse Bayesian learning framework was built to capture the model parameters, i.e., the off-Grid Bias and the CFO. While in the learning process, a damped generalized approximate message passing algorithm was introduced to obtain accurate needed posterior statistics. Finally, simulation results are exhibited to certify the performance of our proposed scheme.
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Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction and Restoration
arXiv: Information Theory, 2019Co-Authors: Muye Li, Shun Zhang, Nan Zhao, Weile Zhang, Xianbin WangAbstract:On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-Grid refinement. Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-Grid Bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-Grid Bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.
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Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction, and Restoration
IEEE Transactions on Communications, 2019Co-Authors: Muye Li, Shun Zhang, Nan Zhao, Weile Zhang, Xianbin WangAbstract:To estimate time-varying MIMO channel at base station, traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to extremely high overhead in massive MIMO systems. To tackle this problem, we propose a novel scheme for DL channel tracking in this paper. First, by utilizing virtual channel representation (VCR), we develop a dynamic uplink (UL) massive MIMO channel model with the consideration of off-Grid refinement. Then, a coordinate-wise expectation maximization (EM) algorithm is adopted for capturing model parameters, including the spatial signatures, time-correlation factors, off-Grid Bias, channel power, and noise power. By exploiting the UL/DL angle reciprocity, the spatial signatures, time-correlation factors and off-Grid Bias of the DL channel model can be reconstructed with the knowledge of UL. However, channel power and noise power are closely related with the carrier frequency, which cannot be perfectly inferred from the UL. Instead of discovering these two parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with partial prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.
Weile Zhang - One of the best experts on this subject based on the ideXlab platform.
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CSPS - Massive MIMO Channel Estimation via Generalized Approximate Message Passing.
Lecture Notes in Electrical Engineering, 2020Co-Authors: Muye Li, Weile Zhang, Shun ZhangAbstract:In this paper, we proposed a channel estimation scheme for an off-Grid massive MIMO channel model, with the consideration of carrier frequency offset at the BS antenna array. We first developed an off-Grid channel model for the spatial sample mismatching problem. Then, an EM based sparse Bayesian learning framework was built to capture the model parameters, i.e., the off-Grid Bias and the CFO. While in the learning process, a damped generalized approximate message passing algorithm was introduced to obtain accurate needed posterior statistics. Finally, simulation results are exhibited to certify the performance of our proposed scheme.
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Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction and Restoration
arXiv: Information Theory, 2019Co-Authors: Muye Li, Shun Zhang, Nan Zhao, Weile Zhang, Xianbin WangAbstract:On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-Grid refinement. Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-Grid Bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-Grid Bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.
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Time-Varying Massive MIMO Channel Estimation: Capturing, Reconstruction, and Restoration
IEEE Transactions on Communications, 2019Co-Authors: Muye Li, Shun Zhang, Nan Zhao, Weile Zhang, Xianbin WangAbstract:To estimate time-varying MIMO channel at base station, traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to extremely high overhead in massive MIMO systems. To tackle this problem, we propose a novel scheme for DL channel tracking in this paper. First, by utilizing virtual channel representation (VCR), we develop a dynamic uplink (UL) massive MIMO channel model with the consideration of off-Grid refinement. Then, a coordinate-wise expectation maximization (EM) algorithm is adopted for capturing model parameters, including the spatial signatures, time-correlation factors, off-Grid Bias, channel power, and noise power. By exploiting the UL/DL angle reciprocity, the spatial signatures, time-correlation factors and off-Grid Bias of the DL channel model can be reconstructed with the knowledge of UL. However, channel power and noise power are closely related with the carrier frequency, which cannot be perfectly inferred from the UL. Instead of discovering these two parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with partial prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.
