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This paper proposes a new chaotic cryptosystem for the encryption of very high-resolution digital images based on the design of a digital chaos generator by using arbitrary precision arithmetic. This can be taken as an alternative to reduce the dynamic degradation that chaotic models present when they are implemented in digital devices and to increase the security of the cryptosystems. The obtained...
Three methods for extracting the behavioral modeling coefficients of the memory polynomial model are compared herein. The first one is the ordinary least square regression, which is widely used for adjusting model parameters; the second is the order recursive least squares, which is suitable for exploring the optimal nonlinearity order and memory depth by comparing subsequent errors while increasing...
This paper aims on three different behavioral models with memory for radio frequency power amplifiers. These models are based on the principle of Volterra series, which were simulated in the Matlab-Simulink environment and implemented on a DSP-FPGA Altera Stratix III board. The MPM, Hammerstein and Wiener models were compared based on the distortion curves AM-AM and AM-PM of a RF-PA 10W through different...
This article presents an adaptive approach system to model the RF power amplifier behavior, taking into account memory effects and nonlinearities. These models are based in an offline training by applying an ANFIS, additionally the model performance is compared with a MPM traditional technique. The ANFIS using 10 or more epochs achieves a lower NMSE. The evaluation of the proposed ANFIS learning system...
This paper presents the design methodology of a complete digital pre-distortion system that enables the power amplifier linearization. This system employs the memory polynomial model for its realization. The performance of the linearization is validated by using an LTE carrier signal in the band of 10 MHz. This integrated solution is capable of linearizing any real power amplifier from measurements...
This paper is focused on the development of an emulation of a Memorial Polynomial Model (MPM) for Radio Frequency Power Amplifier (PA) with Artificial Neural Network (ANN) using back propagation algorithm (BP) considering the nonlinear and memory effects. This model is based on the accurate capacities of artificial neural networks to fit functions. We demonstrate that it is a practical tool to emulate...
In this paper is fully proved the behavior of the Volterra Series through a Memory-Polynomial Model (MPM) excited by QAM Input and implemented in an Radio Frequency Satellite Link (RFSL). Several sequences of 4-, 8-, 16-, 32 and 64-QAM Inputs were introduced to the MPM. The MPM as special truncation of the Volterra Series showed a orrect behaviour for each QAM Input and the RFSL was able to stabilize...
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