Abstract-Single-carrier (SC) block transmission with frequency-domain equalization (FDE) offers a viable transmission technology for combating the adverse effects of long dispersive channels encountered in high-rate broadband wireless communication systems. However, for high-bandwidth efficiency and high-power-efficiency systems, the channel can generally be modeled by the Hammerstein system, which includes the nonlinear distortion effects of the high-power amplifier (
Abstract-Complex-valued (CV) B-spline neural network approach offers a highly effective means for identifying and inverting practical Hammerstein systems. Compared to its conventional CV polynomial based counterpart, CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. This paper reviews the optimality of CV B-spline neural network approach. Advantages of B-spline neural network approach as compared to polynomial based modeling approach are extensively discussed, and the effectiveness of CV neural network based approach is demonstrated in a real-world application. More specifically, we evaluate the comparative performance of the CV B-spline and polynomial based approaches for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Our results confirm the superior performance of the CV B-spline based NIFDDFE over its CV polynomial based counterpart.Index Terms-Complex-valued (CV) B-spline neural network, CV polynomial model, identification and inversion of Hammerstein channels, nonlinear iterative frequency-domain decision feedback equalization I. INTRODUCTION In many real-world applications, the underlying system that generates complex-valued (CV) signals can be modeled by the CV Hammerstein model. The system is grey-box, as its structure is known to be consisting of an unknown static nonlinearity followed by an unknown linear dynamic model. A well-known example of CV Hammerstein systems is the single-carrier (SC) block transmission communication channel with nonlinear high power amplifier (HPA) at transmitter, whereby the CV static nonlinearity of the Hammerstein system is constituted by the nonlinear transmit HPA, and its linear dynamic subsystem is the dispersive channel which can usually be modeled as a finite-duration impulse response (FIR) filter. Effective identification and inversion of CV Hammerstein systems is therefore crucial in these practical applications.CV B-spline neural network has widely been used as an effective means for identification and inversion of CV
This paper investigates the utilization of wavelet filters via multistage convolution by Reverse Biorthogonal Wavelets (RBW) in high and low pass band frequency parts of speech signal. Speech signal is decomposed into two pass bands of frequency; high and low, and then the noise is removed in each band individually in different stages via wavelet filters. This approach provides better outcomes because it does not cut the speech information, which occurs when utilizing conventional thresholding. We tested the proposed method via several noise probability distribution functions. Subjective evaluation is engaged in conjunction with objective evaluation to accomplish optimal investigation method. The method is simple but has surprise high quality results. The method shows superiority over Donoho and Johnstone thresholding method and BirgeMassart thresholding strategy method.
Abstract-We propose a nonlinear hybrid decision feedback equalizer (NHDFE) for single-carrier (SC) block transmission systems with nonlinear transmit high power amplifier (HPA), which significantly outperforms our previous nonlinear SC frequency-domain equalization (NFDE) design. To obtain the coefficients of the channel impulse response (CIR) as well as to estimate the nonlinear mapping and the inverse nonlinear mapping of the HPA, we adopt a complex-valued (CV) B-spline neural network approach. Specifically, we use a CV B-spline neural network to model the nonlinear HPA, and we develop an efficient alternating least squares scheme for estimating the parameters of the Hammerstein channel, including both the CIR coefficients and the parameters of the CV B-spline model. We also adopt another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can be estimated using the least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. The effectiveness of our NHDFE design is demonstrated in a simulation study, which shows that the NHDFE achieves a signalto-noise ratio gain of 4 dB over the NFDE at the bit error rate level of 10 −4 .Index Terms-Single-carrier block transmission, decision feedback equalizer, nonlinear high power amplifier, Hammerstein channel, complex-valued B-spline neural network
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