The Experts below are selected from a list of 258 Experts worldwide ranked by ideXlab platform
Wolfgang Utschick - One of the best experts on this subject based on the ideXlab platform.
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minimum Mean Square Error vector precoding
European Transactions on Telecommunications, 2008Co-Authors: D A Schmidt, M Joham, Wolfgang UtschickAbstract:We derive the minimum Mean Square Error (MMSE) solution to vector precoding for frequency flat multiuser scenarios with a centralised multi-antenna transmitter. The receivers employ a modulo operation, giving the transmitter the additional degree of freedom to choose a perturbation vector. Similar to existing vector precoding techniques, the optimum perturbation vector is found with a closest point search in a lattice. The proposed MMSE vector precoder does not, however, search for the perturbation vector resulting in the lowest unscaled transmit power, as proposed in all previous contributions on vector precoding, but finds an optimum compromise between noise enhancement and residual interference. We present simulation results showing that the proposed technique outperforms existing vector precoders, as well as the MMSE Tomlinson-Harashima precoder, and compare the turbo-coded performance to the capacity of the broadcast channel. Copyright © 2007 John Wiley & Sons, Ltd.
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minimum Mean Square Error vector precoding
Personal Indoor and Mobile Radio Communications, 2005Co-Authors: D A Schmidt, M Joham, Wolfgang UtschickAbstract:We derive the minimum Mean Square Error (MMSE) solution to vector precoding for frequency flat multiuser scenarios with a centralized multi-antenna transmitter. The receivers employ a modulo operation, giving the transmitter the additional degree of freedom to choose a perturbation vector. Similar to existing vector precoding techniques, the optimum perturbation vector is found with a closest point search in a lattice. The proposed MMSE vector precoder does not, however, search for the perturbation vector resulting in the lowest transmit energy, as proposed in all previous contributions on vector precoding, but finds an optimum compromise between noise enhancement and residual interference. We present simulation results showing that the proposed technique outperforms existing vector precoders, as well as the MMSE Tomlinson-Harashima precoder
Piet M.t. Broersen - One of the best experts on this subject based on the ideXlab platform.
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Mean Square Error of the Empirical Transfer Function Estimator for stochastic input signals
Automatica, 2003Co-Authors: Piet M.t. BroersenAbstract:The accuracy of the Empirical Transfer Function Estimator (ETFE) with stochastic input has been described with a deterministic and a stochastic approach. The results for bias and variance are different, but the Mean Square Error is infinite in both approaches. In the deterministic approach where the input is considered as a single realization of a stochastic process, a number of frequencies has no finite ETFE estimate. The average number of problematic frequencies will be constant if the number of observations increases.
D A Schmidt - One of the best experts on this subject based on the ideXlab platform.
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minimum Mean Square Error vector precoding
European Transactions on Telecommunications, 2008Co-Authors: D A Schmidt, M Joham, Wolfgang UtschickAbstract:We derive the minimum Mean Square Error (MMSE) solution to vector precoding for frequency flat multiuser scenarios with a centralised multi-antenna transmitter. The receivers employ a modulo operation, giving the transmitter the additional degree of freedom to choose a perturbation vector. Similar to existing vector precoding techniques, the optimum perturbation vector is found with a closest point search in a lattice. The proposed MMSE vector precoder does not, however, search for the perturbation vector resulting in the lowest unscaled transmit power, as proposed in all previous contributions on vector precoding, but finds an optimum compromise between noise enhancement and residual interference. We present simulation results showing that the proposed technique outperforms existing vector precoders, as well as the MMSE Tomlinson-Harashima precoder, and compare the turbo-coded performance to the capacity of the broadcast channel. Copyright © 2007 John Wiley & Sons, Ltd.
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minimum Mean Square Error vector precoding
Personal Indoor and Mobile Radio Communications, 2005Co-Authors: D A Schmidt, M Joham, Wolfgang UtschickAbstract:We derive the minimum Mean Square Error (MMSE) solution to vector precoding for frequency flat multiuser scenarios with a centralized multi-antenna transmitter. The receivers employ a modulo operation, giving the transmitter the additional degree of freedom to choose a perturbation vector. Similar to existing vector precoding techniques, the optimum perturbation vector is found with a closest point search in a lattice. The proposed MMSE vector precoder does not, however, search for the perturbation vector resulting in the lowest transmit energy, as proposed in all previous contributions on vector precoding, but finds an optimum compromise between noise enhancement and residual interference. We present simulation results showing that the proposed technique outperforms existing vector precoders, as well as the MMSE Tomlinson-Harashima precoder
Xiaoping Chen - One of the best experts on this subject based on the ideXlab platform.
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fast communication steady state Mean Square Error analysis of regularized normalized subband adaptive filters
Signal Processing, 2013Co-Authors: Xiaoping ChenAbstract:The normalized subband adaptive filter (NSAF) has faster convergence rate than the normalized least-Mean-Square (NLMS) algorithm for colored input signals. Regularization of the NSAF is of importance in practical applications. In this paper, we analyze the steady-state Mean-Square Error (MSE) of regularized NSAFs. The analysis is carried out based on the derivation of a variable regularization matrix NSAF (VRM-NSAF). Theoretical expressions for the steady-state MSE of two regularized NSAFs are derived under some assumptions. Simulation results are given to support the theoretical analysis.
Marc Antonini - One of the best experts on this subject based on the ideXlab platform.
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Mean Square Error Approximation for Wavelet-based Semiregular Mesh Compression
IEEE Transactions on Visualization and Computer Graphics, 2006Co-Authors: Frédéric Payan, Marc AntoniniAbstract:The objective of this paper is to propose an efficient model-based bit allocation process optimizing the performances of a wavelet coder for semiregular meshes. More precisely, this process should compute the best quantizers for the wavelet coefficient subbands that minimize the reconstructed Mean Square Error for one specific target bitrate. In order to design a fast and low complex allocation process, we propose an approximation of the reconstructed Mean Square Error relative to the coding of semiregular mesh geometry. This Error is expressed directly from the quantization Errors of each coefficient subband. For that purpose, we have to take into account the influence of the wavelet filters on the quantized coefficients. Furthermore, we propose a specific approximation for wavelet transforms based on lifting schemes. Experimentally, we show that, in comparison with a “naive” approximation (depending on the subband levels), using the proposed approximation as distortion criterion during the model-based allocation process improves the performances of a wavelet-based coder for any model, any bitrate, and any lifting scheme.
