G.T. Zhou

A robust digital baseband predistorter constructed using memory polynomials

Power amplifiers (PAs) are inherently nonlinear devices and are used in virtually all communications systems. Digital baseband predistortion is a highly cost-effective way to linearize PAs, but most existing architectures assume that the PA has a memoryless nonlinearity. For wider bandwidth applications such as wideband code-division multiple access (WCDMA) or wideband orthogonal frequency-division multiplexing (W-OFDM), PA memory effects can no longer be ignored, and memoryless predistortion has limited effectiveness. In this paper, instead of focusing on a particular PA model and building a corresponding predistorter, we focus directly on the predistorter structure. In particular, we propose a memory polynomial model for the predistorter and implement it using an indirect learning architecture. Linearization performance is demonstrated on a three-carrier WCDMA signal.

Estimation and equalization of fading channels with random coefficients

Abstract The time-varying tap coefficients of frequency-selective fading channels are typically modeled as random processes with low-pass power spectra. However, traditional adaptive techniques usually make no assumption on the channels time variations and hence do not exploit this information. In this paper, Kalman filtering methods are derived to track the channel by employing a multichannel autoregressive description of the time-varying taps in a decision-feedback equalization framework. Fitting a model to the variations of the channels taps is a challenging task because the tap coefficients are not observed directly. Higher-order statistics are employed in this paper in order to estimate the model parameters from input/output data. Consistency of the proposed method is shown, and some illustrative simulations are presented.

A first-order statistical method for channel estimation

Multipath is a major impairment in a wireless communications environment, and channel estimation algorithms are of interest. We propose a superimposed periodic pilot scheme for finite-impulse response (FIR) channel estimation. A simple first-order statistic is used, and any FIR channel can be estimated. There is no loss of information rate but a controllable increase in transmission power. We derive the variance expression of our linear channel estimate and compare with the Cramer-Rao bound. Numerical examples illustrate the effectiveness of the proposed method.

Effects of even-order nonlinear terms on power amplifier modeling and predistortion linearization

Power amplifier (PA) is an essential component in communication systems and is nonlinear in nature. Digital baseband predistortion is an emerging cost effective approach to linearize a PA. To study PA nonlinear characteristics and to construct a predistorter, accurate nonlinear models are often necessary. Polynomials have been used extensively for modeling the behavior of the PA or the predistorter. For bandpass communication signals, attention has been paid mainly to odd-order nonlinear terms. In this paper, we reveal the benefit of including even-order nonlinear terms in baseband modeling of the PA and in enhancing predistortion performance.

Comparing Selected Mapping and Partial Transmit Sequence for PAR Reduction

Selected mapping (SLM) and partial transmit sequence (PTS) are two existing distortionless peak-to-average power ratio (PAR) reduction schemes that have been proposed for orthogonal frequency division multiplexing (OFDM). Previously, it was argued that SLM and PTS have comparable PAR reduction performance but that the latter has lower computational complexity because it uses fewer IFFTs. In this paper, we show that the overall computational complexity of PTS is only lower than that of SLM in certain cases, and that SLM always has better PAR reduction performance. We compare the two schemes using three different performance metrics by assuming a given amount of computational complexity that can be afforded. Using the metrics, we show that SLM outperforms PTS for a given amount of complexity.

On polynomial phase signals with time-varying amplitudes

We address the parameter estimation problem for a class of nonstationary signals modeled as polynomial phase signals with time-varying amplitudes. Exponentially damped polynomial phase signals are treated as a special case and are analyzed in detail. High-order instantaneous moments provide the basic analytical tool, but links are shown to exist with either the usually employed FFT-based technique or the high-resolution Kumaresan-Tufts (1982), MUSIC, and matrix pencil methods. Asymptotic properties of the relevant estimators are established, Cramer-Rao lower bounds on the amplitude and phase parameter estimates are derived, and computer simulations are carried out to evaluate the performance of various schemes. We focus on parametric modeling of AM-FM signals, mainly because parametric techniques offer parsimony and allow for theoretically unlimited resolution.

Harmonics in multiplicative and additive noise: performance analysis of cyclic estimators

Multiplicative noise causes smearing of spectral lines and thus hampers frequency estimation relying on conventional spectral analysis. In contrast, cyclic mean and correlation statistics have proved to be useful for harmonic retrieval in the presence of multiplicative and additive noise of arbitrary color and distribution. Performance analysis of cyclic estimators is carried through both for nonzero and zero mean multiplicative noises. Cyclic estimators are shown to be asymptotically equivalent to certain nonlinear least squares estimators, and are also compared with the maximum likelihood ones. Large sample variance expressions of the cyclic estimators are derived and compared with the corresponding Cramer-Rao bounds when the noises are white Gaussian. It is demonstrated that previously well established results on constant amplitude harmonics are special cases of the present analysis. Simulations not only validate the large sample performance analysis, but also provide concrete examples regarding relative statistical efficiency of the cyclic estimators. >

Memory polynomial predistorter based on the indirect learning architecture

Power amplifiers (PAs) are inherently nonlinear devices and are used in virtually all communications systems. Digital baseband predistortion is a highly cost effective way to linearize PAs, but most existing architectures assume that the PA has a memoryless nonlinearity. For wider bandwidth applications such as WCDMA, PA memory effects can no longer be ignored, and memoryless predistortion has limited effectiveness. In this paper, instead of focusing on a particular PA model and building a corresponding predistorter, we focus directly on the predistorter structure. In particular, we propose a memory polynomial model for the predistorter and implement it using an indirect learning architecture. Linearization performance is demonstrated on a 3-carrier UMTS signal.

Predicting spectral regrowth of nonlinear power amplifiers

Power amplifiers (PAs) are important elements in communications systems and they are inherently nonlinear. Nonlinearity generates spectral regrowth (i.e., spectral broadening) in digitally modulated signals which causes adjacent channel interference. In this paper, we present a closed-form expression for the auto-covariance function of the PA output, whose Fourier transform yields the output power spectral density (PSD). The PA input does not need to be Gaussian and the PSD calculation can be carried out using common PA descriptions such as the AM/AM and AM/PM characteristics. We assume that the input is narrow-band, thus PA memory effects, on the order of a symbol period, are not present. The analytical results allow us to predict spectral regrowth without running expensive or time-consuming time-domain simulations; they also lead to a variety of optimization possibilities in transmitter design that includes the PAs.

Orthogonal polynomials for complex Gaussian processes

Power amplifiers are the major source of nonlinearity in communications systems. Such nonlinearity causes spectral regrowth as well as in-band distortion, which leads to adjacent channel interference and increased bit error rate. Polynomials are often used to model the nonlinear power amplifier or its predistortion linearizer. In this paper, we present a novel set of orthogonal polynomials for baseband Gaussian input to replace the conventional polynomials and show how they alleviate the numerical instability problem associated with the conventional polynomials. The orthogonal polynomials also provide an intuitive means of spectral regrowth analysis.