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2 results on '"Mei Yen Cheong"'

1. Adaptive Piecewise Linear Predistorters for Nonlinear Power Amplifiers With Memory

Author

Stefan Werner, Risto Wichman, Juan Cousseau, Jose Luis Figueroa, M.J. Bruno, and Mei Yen Cheong

Subjects
DIRECT LEARNING, Polynomial, Telecomunicaciones, ADAPTIVE PREDISTORTER, Estimation theory, Stochastic process, Function (mathematics), INGENIERÍAS Y TECNOLOGÍAS, Piecewise linear function, Nonlinear system, Noise, Control theory, DIGITAL PREDISTORTER, Baseband, Electrical and Electronic Engineering, Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información, Mathematics
Abstract

We propose novel direct and indirect learning predistorters (PDs) that employ a new baseband simplicial canonical piecewise linear (SCPWL) function. The performance of the proposed PDs is easily controlled by varying the number of segments of the SCPWL function. When comparing to polynomial-based PDs, our SCPWL-based PDs are more robust for modeling strong nonlinearities and are less sensitive to input noise. In particular, we show that noise appearing in the feedback path of an indirect learning SCPWL-PD has negligible effect on the performance while the polynomial counterpart suffers from a noise-induced coefficient bias. We consider adaptive implementations of both Hammerstein-based and memory-based SCPWL PDs; the former featuring less parameters to be identified while the latter renders more straightforward parameter identification. When deriving the PD algorithms, we avoid a separate PA identification step which allows for a true real-time, or sample-by-sample, implementation without an alternating PA and PD identification procedure. However, to arrive at efficient sample-by-sample algorithms for Hammerstein PDs we need to bypass the problem of the associated nonconvex cost function. This is done by employing a modified, linear-in-the-parameter, Wiener model whose parameters can be explicitly or implicitly used for both indirect and direct learning. Extensive simulations confirm that the proposed SCPWL PDs outperform their polynomial counterparts, especially when noise is present in the feedback path of the indirect learning structure. The same is also verified by circuit level simulations on the Freescale MRF6S23100H class-AB PA in an 802.16d WiMAX system. Fil: Cheong, Mei Yen. Alto University. School of Electrical Engineering; Finlandia Fil: Werner, Stefan. Alto University. School of Electrical Engineering; Finlandia Fil: Bruno, Marcelo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina Fil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina Fil: Cousseau, Juan Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina Fil: Wichman, Risto Ilari. Alto University. School of Electrical Engineering; Finlandia

Published
2012

2. Adaptive Piecewise Linear Predistorters for Nonlinear Power Amplifiers With Memory.

Author

Mei Yen Cheong, Werner, S., Bruno, M. J., Figueroa, J. L., Cousseau, J. E., and Wichman, R.

Subjects
*POWER amplifiers, *ANALOG electronic systems, *ELECTRONIC amplifiers, *DIGITAL electronics, *INTEGRATED circuits, *COMPLEMENTARY metal oxide semiconductors, *ELECTRONIC circuits, *PHOTOVOLTAIC cells
Abstract

We propose novel direct and indirect learning predistorters (PDs) that employ a new baseband simplicial canonical piecewise linear (SCPWL) function. The performance of the proposed PDs is easily controlled by varying the number of segments of the SCPWL function. When comparing to polynomial-based PDs, our SCPWL-based PDs are more robust for modeling strong nonlinearities and are less sensitive to input noise. In particular, we show that noise appearing in the feedback path of an indirect learning SCPWL-PD has negligible effect on the performance while the polynomial counterpart suffers from a noise-induced coefficient bias. We consider adaptive implementations of both Hammerstein-based and memory-based SCPWL PDs; the former featuring less parameters to be identified while the latter renders more straightforward parameter identification. When deriving the PD algorithms, we avoid a separate PA identification step which allows for a true real-time, or sample-by-sample, implementation without an alternating PA and PD identification procedure. However, to arrive at efficient sample-by-sample algorithms for Hammerstein PDs we need to bypass the problem of the associated nonconvex cost function. This is done by employing a modified, linear-in-the-parameter, Wiener model whose parameters can be explicitly or implicitly used for both indirect and direct learning. Extensive simulations confirm that the proposed SCPWL PDs outperform their polynomial counterparts, especially when noise is present in the feedback path of the indirect learning structure. The same is also verified by circuit level simulations on the Freescale MRF6S23100H class-AB PA in an 802.16d WiMAX system. [ABSTRACT FROM PUBLISHER]

Published
2012
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