Title :
Adaptive Piecewise Linear Predistorters for Nonlinear Power Amplifiers With Memory
Author :
Mei Yen Cheong ; Werner, Stefan ; Bruno, M.J. ; Figueroa, Jose Luis ; Cousseau, Juan E. ; Wichman, Risto
Author_Institution :
Dept. of Signal Process. & Acoust., Aalto Univ., Aalto, Finland
fDate :
7/1/2012 12:00:00 AM
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.
Keywords :
WiMax; circuit feedback; circuit simulation; nonlinear network analysis; parameter estimation; piecewise linear techniques; power amplifiers; stochastic processes; Freescale MRF6S23100H class-AB PA; Hammerstein-based SCPWL PD; IEEE 802.16d; PD algorithms; SCPWL function; WiMAX system; Wiener model; adaptive piecewise linear predistorters; baseband simplicial canonical piecewise linear function; circuit level simulations; direct learning; feedback path; indirect learning; indirect learning SCPWL-PD; input noise; linear-in-the-parameter; memory-based SCPWL PD; noise-induced coefficient bias; nonconvex cost function problem; nonlinear power amplifiers; parameter identification; sample-by-sample algorithms; Adaptation models; Baseband; Computational modeling; Mathematical model; Noise; Numerical models; Polynomials; Adaptive predistorter; digital predistorter; direct learning; indirect learning; piecewise linear function; power amplifier with memory;
Journal_Title :
Circuits and Systems I: Regular Papers, IEEE Transactions on
DOI :
10.1109/TCSI.2011.2177007