Browsing by Author "Chung, PJ"

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A frequency-domain SIR maximizing time-domain equalizer for VDSL systems
(2005)
Lin, YP
;
Liang, LH
;
Chung, PJ
;
Phoong, SM
;
電控工程研究所
In this paper we propose a semi-blind time domain equalizer (TEQ) design method that maximize SIR (signal-to-interference ratio) in frequency domain for VDSL systems. The proposed method exploits the training symbols in VDSL initialization using an eigen approach. Unlike earlier eigen based TEQ designs, the proposed method does not require the channel impulse response. The TEQ can be obtained by computing an eigenvector of a positive definite matrix that depends only on the averaged received VDSL symbols. Examples will be given to demonstrate that the proposed TEQ design method can effectively shorten the channel impulse response, and achieve very good bit rates with only a small number of training symbols.
Multiple signal detection using the Benjamini-Hochberg procedure
(2005)
Chung, PJ
;
Bohme, JF
;
Mecklenbrauker, CF
;
Hero, AO
;
電子工程學系及電子研究所
We treat the detection problem for multiple signals embedded in noisy observations from a sensor array as a multiple hypothesis test based on log-likelihood ratios. To control the global level of the multiple test, we apply the false discovery rate (FDR) criterion proposed by Benjamini and Hochberg. The power of this multiple test has been investigated through narrow band simulations in previous studies. Here we extend the proposed method to broadband signals. Unlike the narrow band case where the test statistics are characterized by F-distribution, in the broadband case the test statistics have no closed form distribution function. We apply the bootstrap technique to overcome this dif culty pound. Simulations show that the FDR-controlling procedure always provides more powerful results than the FWE controlling procedure. Furthermore, the reliability of the proposed test is not affected by the gain in power.
Recursive EM and SAGE-inspired algorithms with application to DOA estimation
(2005-08-01)
Chung, PJ
;
Bohme, JF
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
This paper is concerned with recursive estimation using augmented data. We study two recursive procedures closely linked with the well-known expectation and maximization (EM) and space alternating generalized EM (SAGE) algorithms. Unlike iterative methods, the recursive EM and SAGE-inspired algorithms give a quick update on estimates given new data. Under mild conditions, estimates generated by these procedures are strongly consistent and asymptotically normally distributed. These mathematical properties are valid for a broad class of problems. When applied to direction of arrival (DOA) estimation, the recursive EM and SAGE-inspired algorithms lead to a very simple and fast implementation of the maximum-likelihood (ML) method. The most complicated computation in each recursion is inversion of the augmented information matrix. Through data augmentation, this matrix is diagonal and easy to invert. More importantly, there is no search in such recursive procedures. Consequently, the computational time is much less than that associated with existing numerical methods for finding ML estimates. This feature greatly increases the potential of the ML approach in real-time processing. Numerical experiments show that both algorithms provide good results with low computational cost.