The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Jian-lin Li - One of the best experts on this subject based on the ideXlab platform.
-
Fast-Convergence Singular Value Decomposition for Tracking Time-Varying Channels in Massive Mimo Systems
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Pei-yun Tsai, Yi Chang, Jian-lin LiAbstract:A fast-convergence singular value decomposition (SVD) algorithm is developed for tracking time-varying channels in massive MIMO precoding/beamforming systems. Since only strong eigen-modes are selected for data transmission in these systems, our SVD algorithm exploits the properties of partial decomposition and temporal correlation. Besides, the proposed self-adjusting Inverse Power Method can achieve fast convergence by modifying the shift according to the intermediate result during each iteration. Furthermore, the singular vectors and values of the desired eigenmodes can be computed simultaneously. Thus, parallel processing is possible to facilitate high-throughput implementation. Compared to the self-Power Method with super linear convergence, the self-adjusting Inverse Power Method has better convergence and lower complexity. Good channel tracking capability is also demonstrated.
-
ICASSP - Fast-Convergence Singular Value Decomposition for Tracking Time-Varying Channels in Massive Mimo Systems
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Pei-yun Tsai, Yi Chang, Jian-lin LiAbstract:A fast-convergence singular value decomposition (SVD) algorithm is developed for tracking time-varying channels in massive MIMO precoding/beamforming systems. Since only strong eigen-modes are selected for data transmission in these systems, our SVD algorithm exploits the properties of partial decomposition and temporal correlation. Besides, the proposed self-adjusting Inverse Power Method can achieve fast convergence by modifying the shift according to the intermediate result during each iteration. Furthermore, the singular vectors and values of the desired eigenmodes can be computed simultaneously. Thus, parallel processing is possible to facilitate high-throughput implementation. Compared to the self-Power Method with super linear convergence, the self-adjusting Inverse Power Method has better convergence and lower complexity. Good channel tracking capability is also demonstrated.
Pei-yun Tsai - One of the best experts on this subject based on the ideXlab platform.
-
Fast-Convergence Singular Value Decomposition for Tracking Time-Varying Channels in Massive Mimo Systems
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Pei-yun Tsai, Yi Chang, Jian-lin LiAbstract:A fast-convergence singular value decomposition (SVD) algorithm is developed for tracking time-varying channels in massive MIMO precoding/beamforming systems. Since only strong eigen-modes are selected for data transmission in these systems, our SVD algorithm exploits the properties of partial decomposition and temporal correlation. Besides, the proposed self-adjusting Inverse Power Method can achieve fast convergence by modifying the shift according to the intermediate result during each iteration. Furthermore, the singular vectors and values of the desired eigenmodes can be computed simultaneously. Thus, parallel processing is possible to facilitate high-throughput implementation. Compared to the self-Power Method with super linear convergence, the self-adjusting Inverse Power Method has better convergence and lower complexity. Good channel tracking capability is also demonstrated.
-
ICASSP - Fast-Convergence Singular Value Decomposition for Tracking Time-Varying Channels in Massive Mimo Systems
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Pei-yun Tsai, Yi Chang, Jian-lin LiAbstract:A fast-convergence singular value decomposition (SVD) algorithm is developed for tracking time-varying channels in massive MIMO precoding/beamforming systems. Since only strong eigen-modes are selected for data transmission in these systems, our SVD algorithm exploits the properties of partial decomposition and temporal correlation. Besides, the proposed self-adjusting Inverse Power Method can achieve fast convergence by modifying the shift according to the intermediate result during each iteration. Furthermore, the singular vectors and values of the desired eigenmodes can be computed simultaneously. Thus, parallel processing is possible to facilitate high-throughput implementation. Compared to the self-Power Method with super linear convergence, the self-adjusting Inverse Power Method has better convergence and lower complexity. Good channel tracking capability is also demonstrated.
Yi Chang - One of the best experts on this subject based on the ideXlab platform.
-
Fast-Convergence Singular Value Decomposition for Tracking Time-Varying Channels in Massive Mimo Systems
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Pei-yun Tsai, Yi Chang, Jian-lin LiAbstract:A fast-convergence singular value decomposition (SVD) algorithm is developed for tracking time-varying channels in massive MIMO precoding/beamforming systems. Since only strong eigen-modes are selected for data transmission in these systems, our SVD algorithm exploits the properties of partial decomposition and temporal correlation. Besides, the proposed self-adjusting Inverse Power Method can achieve fast convergence by modifying the shift according to the intermediate result during each iteration. Furthermore, the singular vectors and values of the desired eigenmodes can be computed simultaneously. Thus, parallel processing is possible to facilitate high-throughput implementation. Compared to the self-Power Method with super linear convergence, the self-adjusting Inverse Power Method has better convergence and lower complexity. Good channel tracking capability is also demonstrated.
-
ICASSP - Fast-Convergence Singular Value Decomposition for Tracking Time-Varying Channels in Massive Mimo Systems
2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2018Co-Authors: Pei-yun Tsai, Yi Chang, Jian-lin LiAbstract:A fast-convergence singular value decomposition (SVD) algorithm is developed for tracking time-varying channels in massive MIMO precoding/beamforming systems. Since only strong eigen-modes are selected for data transmission in these systems, our SVD algorithm exploits the properties of partial decomposition and temporal correlation. Besides, the proposed self-adjusting Inverse Power Method can achieve fast convergence by modifying the shift according to the intermediate result during each iteration. Furthermore, the singular vectors and values of the desired eigenmodes can be computed simultaneously. Thus, parallel processing is possible to facilitate high-throughput implementation. Compared to the self-Power Method with super linear convergence, the self-adjusting Inverse Power Method has better convergence and lower complexity. Good channel tracking capability is also demonstrated.
S Steven J Hulshoff - One of the best experts on this subject based on the ideXlab platform.
-
iterative solution of the random eigenvalue problem with application to spectral stochastic finite element systems
International Journal for Numerical Methods in Engineering, 2006Co-Authors: Clemens V Verhoosel, Miguel A Gutierrez, S Steven J HulshoffAbstract:A new algorithm for the computation of the spectral expansion of the eigenvalues and eigenvectors of a random non-symmetric matrix is proposed. The algorithm extends the deterministic Inverse Power Method using a spectral discretization approach. The convergence and accuracy of the algorithm is studied for both symmetric and non-symmetric matrices. The Method turns out to be efficient and robust compared to existing Methods for the computation of the spectral expansion of random eigenvalues and eigenvectors.
Stefan Werner - One of the best experts on this subject based on the ideXlab platform.
-
EUSIPCO - Recursive total least-squares estimation of frequency in three-phase Power systems
2020Co-Authors: Reza Arablouei, Kutluy ı L Doğançay, Stefan WernerAbstract:1 We propose an adaptive algorithm for estimating the frequency of a three-phase Power system from its noisy voltage readings. We consider a second-order autoregressive linear predictive model for the noiseless complex-valued αβ signal of the system to relate the system frequency to the phase voltages. We use this model and the noisy voltage data to calculate a total least-square (TLS) estimate of the system frequency by employing the Inverse Power Method in a recursive manner. Simulation results show that the proposed algorithm, called recursive TLS (RTLS), outperforms the recursive least-squares (RLS) and the biascompensated RLS (BCRLS) algorithms in estimating the frequency of both balanced and unbalanced three-phase Power systems. Unlike BCRLS, RTLS does not require the prior knowledge of the noise variance.
-
Adaptive frequency estimation of three-phase Power systems
Signal Processing, 2015Co-Authors: Reza Arablouei, Kutluy ı L Doğançay, Stefan WernerAbstract:The frequency of a three-phase Power system can be estimated by identifying the parameter of a second-order autoregressive (AR2) linear predictive model for the complex-valued αβ signal of the system. Since, in practice, both input and output of the AR2 model are observed with noise, the recursive least-squares (RLS) estimate of the system frequency using this model is biased. We show that the estimation bias can be evaluated and subtracted from the RLS estimate to yield a bias-compensated RLS (BCRLS) estimate if the variance of the noise is known a priori. Moreover, in order to simultaneously compensate for the noise on both input and output of the AR2 model, we utilize the concept of total least-square (TLS) estimation and calculate a recursive TLS (RTLS) estimate of the system frequency by employing the Inverse Power Method. Unlike the BCRLS algorithm, the RTLS algorithm does not require the prior knowledge of the noise variance. We prove mean convergence and asymptotic unbiasedness of the BCRLS and RTLS algorithms. Simulation results show that the RTLS algorithm outperforms the RLS and BCRLS algorithms as well as a recently-proposed widely-linear TLS-based algorithm in estimating the frequency of both balanced and unbalanced three-phase Power systems. We show that the recursive least-squares (RLS) estimate of the frequency of a three-phase Power system using the second-order autoregressive (AR2) linear predictive model for the complex-valued αβ signal is biased when the voltage readings are noisy.We show that the frequency estimation bias can be evaluated and subtracted from the RLS estimate to yield a bias-compensated RLS (BCRLS) estimate if the noise variance is known a priori.We also utilize the concept of total least-square (TLS) estimation and calculate a recursive TLS (RTLS) estimate of the system frequency by employing the Inverse Power Method with no need for the prior knowledge of the noise variance.We prove mean convergence and asymptotic unbiasedness of the BCRLS and RTLS algorithms.Simulation results show that the RTLS algorithm outperforms the RLS and BCRLS algorithms as well as a recently-proposed widely-linear TLS-based algorithm in estimating the frequency of both balanced and unbalanced three-phase Power systems.
-
Recursive Total Least-Squares Algorithm Based on Inverse Power Method and Dichotomous Coordinate-Descent Iterations
IEEE Transactions on Signal Processing, 2015Co-Authors: Reza Arablouei, Kutluy ı L Doğançay, Stefan WernerAbstract:We develop a recursive total least-squares (RTLS) algorithm for errors-in-variables system identification utilizing the Inverse Power Method and the dichotomous coordinate-descent (DCD) iterations. The proposed algorithm, called DCD-RTLS, outperforms the previously proposed RTLS algorithms, which are based on the line-search Method, with reduced computational complexity. We perform a comprehensive analysis of the DCD-RTLS algorithm and show that it is asymptotically unbiased as well as being stable in the mean. We also find a lower bound for the forgetting factor that ensures mean-square stability of the algorithm and calculate the theoretical steady-state mean-square deviation (MSD). We verify the effectiveness of the proposed algorithm and the accuracy of the predicted steady-state MSD via simulations.
-
Recursive total least-squares estimation of frequency in three-phase Power systems
2014 22nd European Signal Processing Conference (EUSIPCO), 2014Co-Authors: Reza Arablouei, Kutluy ı L Doğançay, Stefan WernerAbstract:We propose an adaptive algorithm for estimating the frequency of a three-phase Power system from its noisy voltage readings. We consider a second-order autoregressive linear predictive model for the noiseless complex-valued αβ signal of the system to relate the system frequency to the phase voltages. We use this model and the noisy voltage data to calculate a total least-square (TLS) estimate of the system frequency by employing the Inverse Power Method in a recursive manner. Simulation results show that the proposed algorithm, called recursive TLS (RTLS), outperforms the recursive least-squares (RLS) and the bias-compensated RLS (BCRLS) algorithms in estimating the frequency of both balanced and unbalanced three-phase Power systems. Unlike BCRLS, RTLS does not require the prior knowledge of the noise variance.
-
Estimating frequency of three-phase Power systems via widely-linear modeling and total least-squares
2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013Co-Authors: Reza Arablouei, Stefan Werner, Kutluy ı L DoğançayAbstract:The frequency of a three-phase Power system can be estimated from the parameters of a widely-linear predictive model for the complex-valued αβ signal of the system. Using the total least-squares (TLS) Method, it is possible to estimate the model parameters while compensating for error in both input and output of the model when the voltage readings of the three phases are contaminated with noise. In this paper, we utilize the Inverse Power Method to find a TLS estimate of the parameters of the assumed widely-linear predictive model in an adaptive fashion. Simulation results show that the resultant algorithm, called augmented Inverse Power iterations (AIPI), outperforms the recently proposed augmented complex Kalman filter (ACKF) and augmented complex extended Kalman filter (ACEKF) algorithms in estimating the frequency of the three-phase Power systems. Unlike ACKF and ACEKF, AIPI requires no parameter tuning or prior knowledge of the noise variances. Computational complexity of AIPI is also similar to those of ACKF and ACEKF.
