A New Approach to Pruning Volterra Models for Power Amplifiers

Abstract

The objective of this paper is to present an approach to behavioral modeling that can be applied to predict the nonlinear response of power amplifiers with memory. Starting with the discrete-time, complex-baseband full Volterra model, we define a novel methodology that retains only radial branches that can be implemented with one-dimensional finite impulse response filters. This model is subsequently simplified by selecting a subset of directions using an ad-hoc procedure. Both models are evaluated in terms of accuracy in the time and frequency domains and complexity, and are compared with other models described in the literature. The evaluation is conducted using a low-voltage silicon RF driver amplifier and a 5-W PA, which are characterized at different levels with diverse modulation formats, including wideband code-division multiple access (WCDMA) and orthogonal frequency-division multiplexed (OFDM) signals. In all cases, comparison of the measured and simulated responses confirms the effectiveness of the proposed approach.

A Direct Learning Digital Predistortion Algorithm to Accurately Compensate for the Power Amplifier Nonlinearity

“…Therefore, many simplified models have been developed, such as memory polynomial (MP) model [1], [3], generalized memory polynomial (GMP) model [10], radial pruning Volterra (RPV) model [11] and simplified radial pruning Volterra (SRPV) model [11]. Among these models, MP model with even-order terms is quite promising and has been widely used for its simplicity and effectiveness [6]- [11]. To simplify the reverse solving process in our algorithm, the MP model is used here, which can be described as [11] …”

“…Among these models, MP model with even-order terms is quite promising and has been widely used for its simplicity and effectiveness [6]- [11]. To simplify the reverse solving process in our algorithm, the MP model is used here, which can be described as [11] …”

Evolution of Black-Box Models Based on Volterra Series

“…There are other models that take into account some physical knowledge of the device and include important interactions of the input signal, as in [4,22,23]. These are the pruned or reduced VS models, because they include interactions different than the terms in the main diagonal.…”

An Accurate Complexity-Reduced Simplified Volterra Series for Rf Power Amplifiers

“…Volterra series can accurately describe the nonlinearity of PA, but it involves a great number of coefficients [6,7] and is undesirable in practice. Therefore, Volterra-based pruning of the items [8][9][10][11][12][13][14][15][16][17] is essential to practical application. The Memory Polynomial (MP) model [13] contains the diagonal terms of Volterra series.…”