A Weighted Linearization Method for Highly RF-PA Nonlinear Behavior Based on the Compression Region Identification

In this paper, we present an adaptive modeling and linearization algorithm using the weighted memory polynomial model (W-MPM) implemented in a chain involving the indirect learning approach (ILA) as a linearization technique. The main aim of this paper is to offer an alternative to correcting the undesirable effect of spectral regrowth based on modeling and linearization stages, where the 1-dB compression point (P1dB) of a nonlinear device caused by memory effects within a short time is considered. The obtained accuracy is tested for a highly nonlinear behavior power amplifier (PA) properly measured using a field-programmable gate array (FPGA) system. The adaptive modeling stage shows, for the two PAs under test, performances with accuracies of −32.72 dB normalized mean square error (NMSE) using the memory polynomial model (MPM) compared with −38.03 dB NMSE using the W-MPM for the (i) 10 W gallium nitride (GaN) high-electron-mobility transistor (HEMT) radio frequency power amplifier (RF-PA) and of −44.34 dB NMSE based on the MPM and −44.90 dB NMSE using the W-MPM for (ii) a ZHL-42W+ at 2000 MHz. The modeling stage and algorithm are suitably implemented in an FPGA testbed. Furthermore, the methodology for measuring the RF-PA under test is discussed. The whole algorithm is able to adapt both stages due to the flexibility of the W-MPM model. The results prove that the W-MPM requires less coefficients compared with a static model. The error vector magnitude (EVM) is estimated for both the static and adaptive schemes, obtaining a considerable reduction in the transmitter chain. The development of an adaptive stage such as the W-MPM is ideal for digital predistortion (DPD) systems where the devices under test vary their electrical characteristics due to use or aging degradation.

ВИКОРИСТАННЯ АДАПТИВНОГО МОДЕЛЮВАННЯ СТРАТЕГІЙ РОЗВИТКУ ПІДПРИЄМСТВ МАЛОГО БІЗНЕСУ В УМОВАХ ДЕЦЕНТРАЛІЗАЦІЇ

In the context of government reform, small businesses have to look for areas of economic growth, stability and development in local markets. An effective way of such actions is adaptive modeling of development strategies. The purpose of the research substantiation of the need to develop an adaptive strategy for the development of small businesses in changing economic conditions with the identification of a number of criteria for evaluating the results of adaptation, which would indicate the effectiveness of business structures and the benefits of adaptation measures in decentralization. The object of the research is the process of modeling adaptive development strategies in the context of decentralization. The methods of the research – analysis, synthesis, scientific generalization, systems approach. The hypothesis of the research is the assumption that the development of small businesses in terms of government reform should focus on the use of adaptive strategy. The statement of basic materials. In the context of decentralization, small businesses must be able to quickly and efficiently adapt to changes in the market environment. It is thanks to adaptive modeling that companies have the opportunity to get out of such situations with the least risk, because they have their adaptive development strategy ready. During the study, 4 types of adaptive development strategies were identified. In order to choose the perfect strategy, a structural and logical scheme for determining the adaptive development strategy based on the assessment of 5 stages is proposed. The originality and practical significance of the research is determined by the formation of adaptive development strategies with a proposal for the use of methodological tools – the matrix. Conclusions and perspectives of further research. We believe that in terms of OTG, further research will be relevant in the economic and mathematical direction to calculate the feasibility of the proposed adaptive development strategies.