Complex-Chebyshev Functional Link Neural Network Behavioral Model for Broadband Wireless Power Amplifiers

Li, Mingyu; Liu, Jinting; Jiang, Yang; Feng, Wenjiang

doi:10.1109/tmtt.2012.2189239

Cited by 55 publications

(31 citation statements)

References 35 publications

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“…Different DPD models for PAs have been developed and evaluated in recent years. Generally, these reported models can be categorized as the look‐up table model [3–9], Volterra series model [10–15], and neural network model [16–20].…”

Section: Introductionmentioning

confidence: 99%

A new sparse design framework for broadband power amplifier behavioral modeling and digital predistortion

Gong

et al. 2014

IEEJ Transactions Elec Engng

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In this paper, we propose a new sparse framework for the design of the behavioral model and digital predistorter of a broadband power amplifier (PA). We start by formulating the Volterra kernel to multidimensional memory polynomial by considering the high‐order dynamic truncation of the Volterra model. Then we show how an estimate of the most significant coefficients may be obtained using a matching pursuit (MPT) algorithm by exploiting the sparsity of the model. After the indices of the nonzero coefficients are roughly estimated, the block exact Householder inverse QR‐decomposition‐based recursive least squares (HIQRD‐RLS) algorithm is utilized to estimate the sparse model complex coefficients. For broadband nonlinear PAs, the proposed approach is demonstrated to achieve the best performance among the well‐known traditional approaches in terms of in‐band and out‐of‐band specifications. The proposed approach is also validated by evaluating the digital predistortion (DPD) performance on a Class‐AB PA in terms of adjacent channel power ratio (ACPR). © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Introductionmentioning

confidence: 99%

A new sparse design framework for broadband power amplifier behavioral modeling and digital predistortion

Gong

et al. 2014

IEEJ Transactions Elec Engng

Self Cite

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“…For instance, they are faster and more accurate [7][8][9][10][11][12][13][14]. Using ANN to model power amplifiers [15][16][17][18] has become the subject of interest, in the recent years.…”

Section: Introductionmentioning

confidence: 99%

Linearization design method in class-F power amplifier using artificial neural network

et al. 2014

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This paper represents the design of a class-F power amplifier (PA), its artificial neural network (ANN) model and a PA linearization method. The designed PA operates at 1.8 GHz with gain of 12 dB and 1dB output compression point (P1dB) of 36 dBm. The proposed ANN model is used to predict the output power of designed class-F PA as a function of input and DC power. This model utilizes the designed class-F PA as a block, which could be used in a desired linearization circuit. In addition, the power added efficiency (PAE) and the other specifications of a PA, related to power can be predicted using the proposed model. A simple feedforward technique is used to improve the linearity of designed PA. For verification, this linearization method is compared with presented neural network model simulations.The results show the improvement of P1dB from 36 to 41 dBm, which is predicted using the proposed model. Also, the PAE of the final linearized circuit PA is predicted.

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“…The neural network approach has also been investigated as one of the modeling and predistortion techniques for PAs because of its adaptive nature and the claim of a universal approximation capability. Different neural topologies and computation algorithms have been proposed [17][18][19][20][21][22]. Now the ANN-based models are seen as a potential alternative to model RF PAs having mediumto-strong memory effects along with high-order nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

“…of accuracy due to its universal approximation capability. Various topologies of ANNs were reported in the literature for PAs behavioral modeling [17][18][19][20][21][22]. In [17], two separate and uncoupled real-valued neural networks were used to model the output amplitude and phase (or the output I and Q components) with the input signal amplitude as the two neural networks' input, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…Although this topology is simple and computational efficient, it may suffer from the problem of convergence since the two neural networks are trained separately. Alternatively, [21] proposed to apply a complexvalue neural network to the complex signal directly, as shown in Figure 2. In this case, both the weights and activation functions of the network are complex.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Behavioral Modeling of Rf Power Amplifiers With Memory Effects Using Orthonormal Hermite Polynomial Basis Neural Network

Yuan¹,

Feng²

2013

PIER C

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Abstract-Behavioral modeling technique provides an efficient and convenient way to analyze and predict the performance of the RF power amplifiers (PAs) in system-level, and thus helps to construct a suitable predistorter to linearize the PA system. To accurately describe the nonlinear dynamic characteristics of PAs, an orthonormal Hermite polynomial basis neural network (OHPBNN) is utilized to represent the PA behavioral model, which outperforms, mainly in respect of modeling accuracy, the classic feedforward neural network using sigmoid activation functions.In addition, we apply an adaptive algorithm to determine the appropriate memory depth of PA behavioral model. Simulation results show that the proposed model provides more accurate prediction of the PAs output signal compared with classic neural network models.

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Complex-Chebyshev Functional Link Neural Network Behavioral Model for Broadband Wireless Power Amplifiers

Cited by 55 publications

References 35 publications

A new sparse design framework for broadband power amplifier behavioral modeling and digital predistortion

A new sparse design framework for broadband power amplifier behavioral modeling and digital predistortion

Linearization design method in class-F power amplifier using artificial neural network

Behavioral Modeling of Rf Power Amplifiers With Memory Effects Using Orthonormal Hermite Polynomial Basis Neural Network

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