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- Chen-Chu Yeh, John R. Barry
- IEEE Trans. Communications
- 2000

—We consider the design and adaptation of a linear equalizer with a finite number of coefficients in the context of a classical linear intersymbol-interference channel with Gaussian noise and a memoryless decision device. If the number of equalizer coefficients is sufficient, the popular minimum mean-squared-error (MMSE) linear equalizer closely… (More)

The minimum mean-squared-error (MMSE) linear multiuser detector [I-21 is popular because of its good performance and amenability to adaptive implementation. However, there are circumstances in which the linear detector that minimizes bit-error rate (BER) can significantly outperform the MMSE detector. We propose a low-complexity adaptive algorithm for… (More)

- Chen-Chu Yeh, John R. Barry
- IEEE Trans. Signal Processing
- 2003

—We propose the adaptive minimum symbol-error rate algorithm, which is a low-complexity technique for adapting the coefficients of a linear equalizer in systems using pulse-amplitude or quadrature-amplitude modulation. The proposed algorithm very nearly minimizes error probability in white Gaussian noise and can significantly outperform the… (More)

- Abdallah Said Alahmari, Chen-Chu Yeh, Richard Causey, Renato Lopes, Badri Varadarajan, Arvand Nayak +7 others
- 2003

c iii Dedication To My Parents and My Wife iv Acknowledgements I would like to express my gratitude and thanks to Professor John R. Barry for his guidance, support, and encouragement over the past several years. His commitment, continuous advice, and support have been invaluable during these difficult times. I really appreciate all he offered to me as a… (More)

- Chen-Chu Yeh, John R. Barry
- ICC
- 1997

- Frank A. Boyle, Jarvis Haupt, Gerald L. Fudge, Chen-Chu A. Yeh
- 2007 IEEE/SP 14th Workshop on Statistical Signal…
- 2007

Recent theoretical results in Compressive Sensing (CS) show that sparse (or compressible) signals can be accurately reconstructed from a reduced set of linear measurements in the form of projections onto random vectors. The associated reconstruction consists of a nonlinear optimization that requires knowledge of the actual projection vectors. This work… (More)

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