Multi-user multiple-input and multiple-output (MU-MIMO) precoding is an effective transmission scheme for achieving very high spectral efficiency in modern wireless communication systems. Although numerous multi-user ...
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Multi-user multiple-input and multiple-output (MU-MIMO) precoding is an effective transmission scheme for achieving very high spectral efficiency in modern wireless communication systems. Although numerous multi-user precoder designs have been proposed over the last few decades, the signal-to-leakage-and-noise ratio (SLNR)-based precoder is known to achieve favorable performance-complexity tradeoffs. This paper presents a new generalized eigenvalue decomposition (GEVD) processor, that is, the core processing unit dominating the overall complexity in SLNR-based precoders. A low-complexity square-root algorithm is adopted to eliminate the need for matrix multiplications and matrix inversion computations, which reduces the complexity of the GEVD processor considerably. Finally, the proposed processor was designed and implemented by using a 40-nm complementary metal-oxide-semiconductor technology, which exhibited a maximum throughput rate of 1.1-M matrices/s for the MU-MIMO system.
This study reports the application of an adaptively tuned square-root Cubature Kalman filter (SCKF) for the speed and position estimation of a permanent magnet synchronous motor (PMSM) drive. The proposed estimator is...
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This study reports the application of an adaptively tuned square-root Cubature Kalman filter (SCKF) for the speed and position estimation of a permanent magnet synchronous motor (PMSM) drive. The proposed estimator is observed to exhibit improved noise rejection characteristics as compared to the hitherto widely applied extended Kalman filter (EKF) observer. A third degree spherical-radial cubature rule is used in the Cubature Kalman filter (CKF) to numerically compute the multivariate moment integrals of the general Bayesian estimation equation. CKF is a non-linear filter which avoids linearisation and the associated errors. The realisation of CKF using the square-root algorithm results in numerical stability, as with the realisation of EKF using the square-root algorithm. Simulation results are presented for a three-phase inverter-fed PMSM, along with the experimental results. The estimator and the control algorithms are realised on the MATLAB real-time environment, interfaced with the hardware using the National Instruments data acquisition system NI PCI-6221.
In this paper, a novel blind channel estimation algorithm for a multiple input multiple output (MIMO) system is described. This algorithm is easier to implement than the previously reported blind estimation algorithms...
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ISBN:
(纸本)9781424416448
In this paper, a novel blind channel estimation algorithm for a multiple input multiple output (MIMO) system is described. This algorithm is easier to implement than the previously reported blind estimation algorithms because the channel state information (CSI) is recovered without performing complex mathematical operations such as singular value decomposition (SVD) or eigenvalue decomposition (EVD). The ambiguity associated with the proposed blind estimation is reduced to the phase ambiguity. The proposed channel estimation algorithm is accompanied by new block coding scheme. Using this coding scheme, a block encoder is only required at the transmitter. The proposed scheme is spectral efficient, as it offers the full coding rate when the numbers of transmitting and receiving antennas are equal. The validity of the proposed channel estimation algorithm and coding scheme is verified via computer simulations.
We consider the problem of computing the inverse of a large class of infinite systems of linear equations, which are described by a finite set of data. The class consists of equations in which the linear operator is r...
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We consider the problem of computing the inverse of a large class of infinite systems of linear equations, which are described by a finite set of data. The class consists of equations in which the linear operator is represented by a discrete time-varying dynamical system whose local state space is of finite dimension at each time point k, and which reduces to time invariant systems for time points k --> +/-infinity. In this generalization of classical matrix inversion theory, inner-outer factorizations of operators play the role that QR-factorization plays in classical linear algebra. Numerically, they lead to so-called 'squareroot' implementations, for which am-active algorithms can be derived, which do not require the determination of spurious multiple eigenvalues, as would be the case if the problem was converted to a discrete time Riccati equation by squaring. We give an overview of the theory and the derivation of the main algorithms. The theory contains both the standard LTI case and the case of a finite set of linear equations as special instances, a particularly instance of which is called 'matrices of low Hanker rank', recently sometimes called 'quasi-separable matrices'. However, in the general case considered here, new phenomena occur which are not observed in these classical cases, namely the occurrence of 'defect spaces'. We describe these and give an algorithm to compute them as well. In all cases, the algorithms given are linear in the amount of data. (C) 2000 Elsevier Science Inc. All rights reserved.
In a companion paper, a fast transversal filter (FTF) algorithm was derived for solving multichannel multiexperiment recursive least-squares (RLS) problems arising in adaptive FIR filtering. By introducing sequential ...
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In a companion paper, a fast transversal filter (FTF) algorithm was derived for solving multichannel multiexperiment recursive least-squares (RLS) problems arising in adaptive FIR filtering. By introducing sequential processing of the different channels and experiments, the multichannel multiexperiment algorithm was decomposed into a set of intertwined single-channel single-experiment algorithms, resulting in a modular algorithm structure. The algorithm was derived under the prewindowing assumption. However, using an embedding into multichannel and multiexperiment problems, we show how the conventional FTF algorithms for the growing-window and sliding-window covariance cases follow naturally from the modular prewindowed algorithm. Furthermore, taking the sequential processing one step of granularity further, we derive modular multichannel FTF algorithms for these covariance cases also.
A Fast Transversal Filter (FTF) algorithm is proposed for solving multichannel multiexperiment Recursive Least-squares (RLS) problems that exhibit a shift structure between consecutive regression vectors. The sequenti...
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A Fast Transversal Filter (FTF) algorithm is proposed for solving multichannel multiexperiment Recursive Least-squares (RLS) problems that exhibit a shift structure between consecutive regression vectors. The sequential processing of the different channels and experiments decomposes the multichannel multiexperiment algorithm into a set of intertwined single-channel single-experiment algorithms, resulting in a modular algorithm structure. The sequential processing strategy corresponds to a triangular factorization of error covariance matrices and numerical benefits accrue from this approach. Furthermore, recently introduced stabilization techniques for proper control of the propagation of numerical errors in the update recursions of FTF algorithms can also straightforwardly be incorporated. The algorithm is derived under the prewindowing assumption. In a companion paper we show how the proposed algorithm provides a framework of the covariance windowing cases.
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