Memory Effect Modeling of Wideband Wireless Transmitters Using Neural Networks

Liu, Taijun; Yan, Ye; Zeng, Xingbin; Ghannouchi, Fadhel M.

doi:10.1109/iccsc.2008.154

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Behavioral modeling [ 5 , 6 ] of nonlinear circuits and systems has received much attention in recent years. In behavioral modeling, the nonlinear component is generally considered as a “black box,” which is completely characterized by external responses, that is, in terms of input and output signals, through the use of relatively simple mathematical expressions.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of Modeling Based on Improved Elman Neural Network

Shao

Zhao

et al. 2014

The Scientific World Journal

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A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance.

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Section: Introductionmentioning

confidence: 99%

Numerical Analysis of Modeling Based on Improved Elman Neural Network

Shao

Zhao

et al. 2014

The Scientific World Journal

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“…These nonlinearities and the memory effects of PAs introduce distortions in the transmitted signal. The memory effects can be classified into two categories: the electro-thermal memory effects and the electric memory effects that are attributed to the non-constant spectral response around the carrier frequency [1]. These distortions create spectral regrowth in the adjacent channel (ACPR : adjacent channel power ratio) as well as deformations of the modulated signal constellation (EVM : error vector magnitude) [2] in the bandwidth of modern broadband wireless communication systems, such as code division multiple access (cdma2000), wideband code division multiple access (WCDMA) [1], and long term evolution (LTE).…”

Section: Introductionmentioning

confidence: 99%

Power Amplifier Behavioral Modeling by Neural Networks and Their Implementation on FPGA

Ntoune

Bahoura

Park

2012

2012 IEEE Vehicular Technology Conference (VTC Fall)

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In this paper, field programmable gate array (FPGA) implementation of two power amplifier (PA) dynamic behavioral modeling approaches with real-valued time-delay neural network (RVTDNN) and real-valued recurrent neural network (RVRNN) architectures are presented. The proposed PA models are based on the multilayer perceptron (MLP) neural networks with delayed inputs to take into account nonlinearity and memory effects of the PA. The synoptic weights of these neural networks are dynamically updated in order to prevent any eventual change in the PA's characteristics. Both architectures have been optimized to include only six hidden neurons and implemented on FPGA using Xilinx system generator. The FPGA is preferred to the digital signal processor (DSP) because it allows parallel computation tasks and software like flexibility. The modeling performances of these architectures are compared using 16-QAM modulated test signal. The mean square error (MSE) between the desired and the actual outputs of these models are also compared.

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“…The modeling of nonlinear system [2] has become an essential part of system analysis because it provides a convenient and efficient method to predict system-2 level performance without the computational complexity of full system simulation or physical-level analysis, thereby speeding up the analysis process.…”

Section: Introductionmentioning

confidence: 99%

Modeling of nonlinear system based on deep learning framework

et al. 2015

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Abstract.A novel modeling based on deep learning framework which can exactly manifest the characteristics of nonlinear system is proposed in this paper. Specifically, a Deep Reconstruction are the simulated two-tone signal and actual square wave signal. The second system is a permanent magnet synchronous motor (PMSM) servo control system based on fuzzy PID control strategy. In terms of simulated and actual speed curves, BP-RBMs, DRM and ENN model are adopted on comparison respectively. It is shown by experimental results that the proposed model with fewer parameters and iteration number can reconstruct the nonlinear system accurately, and depict the memory effect, the nonlinear distortion and the dynamic performance of system precisely.

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Memory Effect Modeling of Wideband Wireless Transmitters Using Neural Networks

Cited by 4 publications

References 8 publications

Numerical Analysis of Modeling Based on Improved Elman Neural Network

Numerical Analysis of Modeling Based on Improved Elman Neural Network

Power Amplifier Behavioral Modeling by Neural Networks and Their Implementation on FPGA

Modeling of nonlinear system based on deep learning framework

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