https://www.dtc.umn.edu/s/resources/gg-se.pdf

A bibliography on nonlinear system identification

A nonlinear spectral analysis technique that enables digital communication system metrics; SNR, EVM and the waveform quality factor (/spl rho/) to be related to in-band distortion spectrum is presented. System metrics are estimated from the measured output power and in-band distortion power. The estimated metrics are verified by direct measurements of each metric using a vector signal analyzer (VSA) performed on a forward-link IS-95 signal. Estimated system metrics are in excellent agreement with measured values.

Accurate estimation of digital communication system metrics - SNR, EVM and /spl rho/ in a nonlinear amplifier environment

It is shown that, for a Gaussian process and for some non-Gaussian processes, any memoryless nonlinearity has a whitening effect in the sense that the output spectrum is smoother and occupies a greater bandwidth than the input spectrum. The ratio between input and output bandwidths is investigated by using several measures of bandwidth. Also, it is shown that classes of nonlinearities exist that are equivalent in the sense of producing the same spectral transformations.

The effect of a memoryless nonlinearity on the spectrum of a random process

Methods of generating pseudorandom number sequences that might have predetermined spectral and probability distribution functions are discussed. Such sequences are of potential value in Monte Carlo simulation of communication, radar, and allied systems. The methods described are particularly suited to implementation on microcomputers, are machine portable, and have been subjected to exhaustive investigation by means of both statistical and theoretical tests. >

Monte Carlo simulation and random number generation

Nonlinear compensation using power series inverses suffers from various limitations, resulting from the coupling between the nonorthogonal basis elements used to represent the nonlinearity. These problems can be largely overcome by using representations of the nonlinearity and its inverse, based on orthogonal representations. We give a brief discussion of the disadvantages of the power series compensation technique. We then present a technique for composing and inverting orthogonal representations. Using this inversion technique, we derive a nonlinear compensation method that is based on the removal of specific terms in the orthogonal series. Compared with the power series method, this provides much better cancellation, and in some cases complete removal, of selected distortion terms. We illustrate the technique using examples involving narrow-band and broad-band inputs. We then investigate the robustness of the orthogonal compensation approach to variations in input signal properties.

Nonlinear system compensation based on orthogonal polynomial inverses

Using his characteristic-function approach, Rice (1945) obtained a double series for the autocorrelation function of a sinusoidal signal and Gaussian noise after passage through a memoryless nonlinearity. It is shown here that the output of the nonlinearity can be expressed as the sum of uncorrelated terms whose auto-correlation functions are the terms of Rice's double series. Such a decomposition of the output is shown to be generally possible if and only if the bivariate probability density functions of the input signal and the input noise can both be expressed in the diagonal form studied by Barrett and Lampard (1955), though not necessarily involving polynomials, as they can in the sinusoidal and Gaussian cases. In addition, a more direct and meaningful equation is found for the coefficients in Rice's double series.

N. Blachman

Papers

The uncorrelated output components of a nonlinearity