Abstract
The operation of the power amplifier (PA) in wireless transmitters presents a trade-off between linearity and power efficiency, being more efficient when the device exhibits the highest nonlinearity. Its modeling and linearization performance depend on the quality of the underlying Volterra models that are characterized by the presence of relevant terms amongst the enormous amount of regressors that these models generate. The presence of PA mechanisms that generate an internal state variable motivates the adoption of a bivariate Volterra series perspective with the aim of enhancing modeling capabilities through the inclussion of beneficial terms. In this paper, the conventional Volterra-based models are enhanced by the addition of terms, including cross products of the input signal and the new internal variable. The bivariate versions of the general full Volterra (FV) model and one of its pruned versions, referred to as the circuit-knowledge based Volterra (CKV) model, are derived by considering the signal envelope as the internal variable and applying the proposed methodology to the univariate models. A comparative assessment of the bivariate models versus their conventional counterparts is experimentally performed for the modeling of two PAs driven by a 30 MHz 5G New Radio signal: a class AB PA and a class J PA. The results for the digital predistortion of the class AB PA under a direct learning architecture reveal the benefits in linearization performance produced by the bivariate CKV model structure compared to that of the univariate CKV model.
Highlights
Efficiency is of paramount importance in modern wireless communication systems, affecting different aspects of their implementation
We demonstrate the performance improvement attained with the bivariate Volterra model for two cases, a class AB and a class J power amplifier (PA)
Since the extension of nonlinear order and memory length is impracticable for the bivariate full Volterra (FV) model, in the following the focus will be put in the comparison of the conventional and bivariate circuit-knowledge based Volterra (CKV) models with optimized order and memory
Summary
Efficiency is of paramount importance in modern wireless communication systems, affecting different aspects of their implementation. Based on the baseband equivalent Volterra model [7], the discrete-time amplifier output at the fundamental frequency zone is described by a linear combination of regressors These regressors are given by odd-order monomials of the discrete-time input complex envelope x (k ) and its conjugate x ∗ (k ) with different delays that are denoted by q, q1 , q2 , etc. If this univariate Volterra series is truncated to a maximum nonlinear order and memory depth, the resulting structure is denoted as the full Volterra (FV) model. The aptitude of the bivariate models in the design of DPDs to linearize PAs is considered in Section 4 and a summary is presented in the last section
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