A Two-Step Identification Approach for Twin-Box Models of RF Power Amplifier

We propose a two-step identification approach for twin-box model (Wiener or Hammerstein) of RF power amplifier. The linear filter block and the static nonlinearity block are extracted, respectively, based on least-squares method, by iterative calculation. Simulations show that the method can get quite accurate parameters to model different nonlinear models with memory such as Wiener, Hammerstein, Wiener-Hammerstein (W-H), and memory polynomial models, hence, demonstrating its robustness. Furthermore, experimental results show excellent agreement between measured output and modeled output, where one carrier WCDMA signal is used as the excitation for a wideband RF amplifier. 1. Introduction New signal modulation formats in modern communication systems are with high peak to average ratio (PAR) and wide bandwidth. Power amplifiers (PAs) excited by such signals exhibit different nonlinearity and memory effects compared with the case of single-tone excitation. Consequently, the development of behavioral models is indispensable for performance analysis of PAs and system simulation with PAs. The Volterra model [1] can be applied successfully to express PAs characteristics with memory effects, but with very complicated coefficients. With a reduction of the coefficients number, many simplified approximations for Volterra model are Wiener, Hammerstein, Wiener-Hammerstein (W-H), memory polynomial models [1–5]. Especially, the Wiener model and Hammerstein model, known as twin-box models, are the most popular ones. Usually, they can model PAs’ behavior accurately with less complexity. The identification process for the twin-box models is faster than that of Volterra model also. Some previous works on twin-box models identification have been summarized or developed in [6–10]. Reference [6] summarizes different types of methods about Wiener model identification. In [7], the author proposes a new recursive identification method, based on the old one in [8]. In [9], it points out that Hammerstein model permits linear regression. However, identification of Wiener model is more complicated, where estimation of the intermediate variable is performed firstly, and then a two-step estimation of the Wiener coefficients by linear regression is available. The identification method in [10] is based on the artificial intelligence technique of swarm intelligence. In summary, most of these identification methods are either complicated or with low accuracy. In this paper, we consider the twin-box models identification process and propose a novel identification method with simplicity.