Browsing by Author "Lin, Ching-An"

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Blind channel identification for MIMO single carrier zero padding block transmission systems
(2006)
Chen, Yi-Sheng
;
Lin, Ching-An
;
電控工程研究所
;
Institute of Electrical and Control Engineering
We propose a blind identification method for MIMO single carrier zero padding block transmission systems. The method uses periodic precoding on the source signal before transmission. The estimation of channel impulse response matrix consists of two steps: (i) obtain the channel product matrix by solving a lower-triangular linear system and (ii) obtain the channel impulse response matrix by computing the positive eigenvalues and eigenvectors of a Hermitian matrix formed from the channel product matrix. A sufficient condition for identifiability is simply that the channel impulse response matrix is full column rank. The design of the precoding sequence to minimize the noise effect in covariance matrix estimation is proposed. Simulation examples are used to demonstrate the performance of the method.
Blind identification of MIMO channels using optimal periodic precoding
(2007-04-01)
Lin, Ching-An
;
Chen, Yi-Sheng
;
電控工程研究所
;
Institute of Electrical and Control Engineering
We propose a method for blind identification of multiple-input mutiple-out (MIMO) finite-impulse response (FIR) channels that exploits cyclostationarity of the received data induced at the transmitters by periodic precoding. It is shown that, by properly choosing the precoding sequence, the MIMO FIR transfer functions, with M-t inputs and M-r outputs, can be identified up to a unitary matrix ambiguity. The transfer functions need not be irreducible or column reduced, and there can be more outputs (M-r >= M-t) or more inputs (M-r < M-t). The method exploits the linear relation between the covariance matrix of the received data and the "channel product matrices". The method is shown to be robust with respect to channel-order overestimation. The proposed algorithm requires solving linear equations and computing the nonzero eigenvalues and eigenvectors of a Hermitian positive, semidefinite matrix. The performance of the algorithm, and indeed the identifiability, depends on the choice of the precoding sequence. We propose a method for optimal selection of the precoding sequence which takes into account the effect of additive channel noise and numerical error in covariance matrix estimation. Simulation results are used to demonstrate the performance of the algorithm.
Blind-Channel Identification for MIMO Single-Carrier Zero-Padding Block-Transmission Systems
(2008-07-01)
Chen, Yi-Sheng
;
Lin, Ching-An
;
電控工程研究所
;
Institute of Electrical and Control Engineering
We propose a blind identification method for multiple-input multiple-output (MIMO) single-carrier zero-padding block-transmission systems. The method uses periodic precoding on the source signal before transmission. The estimation of the channel impulse response matrix consists of two steps: 1) obtain the channel product matrix by solving a lower-triangular linear system; 2) obtain the channel impulse response matrix by computing the positive eigenvalues and eigenvectors of a Hermitian matrix formed from the channel product matrix. The method is applicable to MIMO channels with more transmitters or more receivers. A sufficient condition for identifiability is simply that the channel impulse response matrix is full column rank. The design of the precoding sequence which minimizes the noise effect in covariance matrix estimation is proposed and the effect of the optimal precoding sequence on channel equalization is discussed. Simulations are used to demonstrate the performance of the method.
Distributed Estimation for Vector Signal in Linear Coherent Sensor Networks
(2012-02-01)
Wu, Chien-Hsien
;
Lin, Ching-An
;
電控工程研究所
;
Institute of Electrical and Control Engineering
We introduce the distributed estimation of a random vector signal in wireless sensor networks that follow coherent multiple access channel model. We adopt the linear minimum mean squared error fusion rule. The problem of interest is to design linear coding matrices for those sensors in the network so as to minimize mean squared error of the estimated vector signal under a total power constraint. We show that the problem can be formulated as a convex optimization problem and we obtain closed form expressions of the coding matrices. Numerical results are used to illustrate the performance of the proposed method.
Linear coherent distributed estimation over unknown channels
(2011-04-01)
Wu, Chien-Hsien
;
Lin, Ching-An
;
電控工程研究所
;
Institute of Electrical and Control Engineering
We study linear distributed estimation with coherent multiple access channel model and MMSE fusion rule. The flat fading channels are assumed unknown at the fusion center and need to be estimated. We adopt a two-phase approach, which first estimates channels and then estimates the source signal, to minimize the MSE of the estimated signal. We study optimal power allocation under a total network power constraint. We consider the optimal power allocation scheme in which training power and data power for each sensor are optimized, and the equal power allocation scheme in which training power is optimized while data power for each sensor is set equal. In both schemes, the problem is formulated as a constrained optimization problem and analytical closed-form solution is obtained. Analytic results reveal that (i) with estimated channels, the MSE approaches to a finite nonzero value as the number of sensors increases: (ii) the optimal training powers are the same in both schemes: (iii) the MSE performance compared with the case when channels are known shows the penalty caused by channel estimation becomes worse as the number of sensors increases. Simulation results verify our findings. (C) 2010 Elsevier B.V. All rights reserved.