ABSTRACT
A good approximation to power amplifier (PA) behavioral modeling requires precise baseband models to mitigate nonlinearities. Since digital predistortion (DPD) is used to provide the PA linearization, a framework is necessary to validate the modeling figures of merit support under signal conditioning and transmission restrictions. A field-programmable gate array (FPGA)-based testbed is developed to measure the wide-band PA behavior using a single-carrier 64-quadrature amplitude modulation (QAM) multiplexed by orthogonal frequency-division multiplexing (OFDM) based on long-term evolution (LTE) as a stimulus, with different bandwidths signals. In the search to provide a heuristic target approach modeling, this paper introduces a feature extraction concept to find an appropriate complexity solution considering the high sparse data issue in amplitude to amplitude (AM-AM) and amplitude to phase AM-PM models extraction, whose penalties are associated with overfitting and hardware complexity in resulting functions. Thus, experimental results highlight the model performance for a high sparse data regime and are compared with a regression tree (RT), random forest (RF), and cubic-spline (CS) model accuracy capabilities for the signal conditioning to show a reliable validation, low-complexity, according to the peak-to-average power ratio (PAPR), complementary cumulative distribution function (CCDF), coefficients extraction, normalized mean square error (NMSE), and execution time figures of merit. The presented models provide a comparison with original data that aid to compare the dimension and robustness for each surrogate model where (i) machine learning (ML)-based and (ii) CS interpolate-based where high sparse data are present, NMSE between the CS interpolated based are also compared to demonstrate the efficacy in the prediction methods with lower convergence times and complexities.
Subject(s)
Amplifiers, Electronic , Equipment DesignABSTRACT
The effect of temperature on the development of Megaselia halterata (Wood) (Diptera: Phoridae) on A15 variety of button mushroom in the stages of casing and spawn-running was investigated at eight constant temperatures (10, 12.5, 15, 18, 20, 22.5, 25, and 27°C) and developmental rates were modeled as a function of temperature. At 25 and 27°C, an average of 22.2 ± 0.14 and 20.0 ± 0.10 days was needed for M. halterata to complete its development from oviposition to adult eclosion in the stages of casing and spawn-running, respectively. The developmental times of males or females at various constant temperatures were significantly different. Among the linear models, the Ikemoto and Takai linear model in the absence of 12.5 and 25°C showed the best statistical goodness-of-fit and based on this model, the lower developmental threshold and the thermal constant were estimated as 10.4°C and 526.3 degree-days, respectively. Twelve nonlinear temperature-dependent models were examined to find the best model to describe the relationship between temperature and development rate of M. halterata. The Logan 10 nonlinear model provided the best estimation for T opt and T max and is strongly recommended for the description of temperature-dependent development of M. halterata.