Augmented Convolutional Neural Network for Behavioral Modeling and Digital Predistortion of Concurrent Multiband Power Amplifiers

Jaraut, Praveen; Abdelhafiz, Abubaker; Chenini, Hanen; Hu, Xin; Helaoui, Mohamed; Rawat, Meenakshi; Chen, Wenhua; Boulejfen, Noureddine; Ghannouchi, Fadhel M.

doi:10.1109/tmtt.2021.3075689

Cited by 33 publications

(12 citation statements)

References 30 publications

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“…Further advance in microwave CAD led to the use of deep neural networks with many hidden layers to address more complex microwave modeling problems. Various deep neural network methods have been explored for microwave CAD, such as deep MLP [89], [90], [91], [142], RNN [133], [140], [150], and CNN [74], [77], [85], [151], [172].…”

Section: Deep Neural Network Techniques For Microwave Modelingmentioning

confidence: 99%

“…An example of CNN for microwave CAD is the augmented CNN, developed for behavioral modeling and digital predistortion of concurrent multiband power amplifiers [172]. To reduce the model complexity and improve the linearization performance of the digital predistortion model, the augmented CNN is composed of an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer.…”

Section: Augmented Cnnmentioning

confidence: 99%

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Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Feng

Jin

et al. 2022

IEEE Trans. Microwave Theory Techn.

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This article presents an overview of artificial neural network (ANN) techniques for a microwave computer-aided design (CAD). ANN-based techniques are becoming useful for performing forward/inverse modeling for active/passive components to enhance a circuit design. With measured or simulated data of microwave devices, ANNs can be trained to learn relevant microwave relationships, which are, otherwise, computationally expensive or for which efficient analytical formulas are not available. Fundamental concepts of the ANN structure and training, such as feedforward neural networks (FFNNs), recurrent neural networks (RNNs)/dynamic neural networks (DNNs)/time-delay neural networks (TDNNs), deep neural networks, and neural network training and extrapolation, are described. Knowledgebased neural networks (KBNNs) are described for improving the accuracy and reliability of modeling and design optimization. Various advanced ANN techniques, such as neuro-transfer function (neuro-TF) modeling, neural network inverse modeling, and deep neural network modeling, are discussed. The existing and emerging applications of ANN in microwave CAD are identified, such as electromagnetic (EM)/multiphysics modeling, modeling of nonlinear circuits and transistors, filter design, very largescale integration (VLSI) interconnects, oscillator, transmitter and receiver modeling, and CAD applications in such as gallium nitride (GaN) high electron-mobility transistor (HEMT), wireless power transfer (WPT), microelectromechanical system (MEMS), and substrate-integrated waveguide (SIW).

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Section: Deep Neural Network Techniques For Microwave Modelingmentioning

confidence: 99%

Section: Augmented Cnnmentioning

confidence: 99%

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Feng

Jin

et al. 2022

IEEE Trans. Microwave Theory Techn.

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“…Average Precision (mAP): When evaluating the detection network, mAP is often an important indicator for analyzing and evaluating models [19][20]. When doing human body pose estimation, the bottom-up estimation method is to first predict the key points of the human body, and then connect the key points.…”

Section: Algorithm Researchmentioning

confidence: 99%

Gymnastic Movement Recognition Based on Depth Convolution Neural Network

2022

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With the development of deep learning technology and mobile Internet, more and more image-based artificial intelligence applications appear in people's lives. In the image information, the understanding of the characters in the picture has always been the focus of research and application, and it is also the basis of human-computer interaction. The human body key point detection technology can detect the joint point position of the target person in the image, so as to provide basic information for subsequent human-computer interaction applications and functions. The main purpose of this paper is to conduct research on gymnastics AR based on deep convolutional neural networks (CNN). This paper first expounds the mechanism contained in each component of the CNN. Analyzing the characteristics of CNN, compared with the multi-layer BP neural network, the transmission between the CNN neurons is combined with the local receptive field through weight sharing, which reduces the weight parameters while maintaining the network depth. The gradient disappearance problem can be avoided in the training process, and its network structure has good generalization ability. Through performance comparison experiments, it is found that whether using single-stream RGB data, optical flow data, or dual-stream fusion results, the recognition accuracy of deep neural networks is better than that of conventional networks. Illustrating the effectiveness of deep CNNs in gymnastics action recognition (AR).

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“…There are different types of modeling for system linearization, and among these neural networks stand [6]- [7]. The Feed Forward topology consists of a network in which data travel in only one direction, from the entrance to the last layer.…”

Section: Introductionmentioning

confidence: 99%

Selective Algorithm for Expanded Group Method of Data Handling Applied to Power Amplifier Modeling

Machado¹,

Nypwipwy²,

França³

et al. 2022

JICS

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Power amplifiers (PAs) are electronic devices commonly used in telecommunications that need to transmit information with high energetic efficiency. For this, it is necessary to use data manipulation methods that assist in the linearization of the output signal. Our previous conference paper presented two codes constructed based on the Group Method of Data Handling (GMDH) and which differ in their way of selecting the best coefficients to be used in the calculations of the neural network. The first method, called Embracing, assumes greater availability of data, while the second, called Selective, selects information from the beginning of the code. This work extends the previous GMDH models by expanding the PA inputs into Laguerre basis functions with a single real pole. The comparison among the different approaches employs experimental data collected from a GaN HEMT class AB PA and a Si LDMOS class AB. The most selective and computationally more complex structure, when searching for identification since from the first layers, expresses minor errors and the best results in the output for both Conventional and Expanded GMDH models, becoming a reasoned option for use in PAs. A normalized mean square error (NMSE) of -35.44 dB was obtained by Expanded GMDH with Selective algorithm and 5 inputs when using the GaN PA, whereas a NMSE of -40.35 dB was obtained by the Expanded GMDH with Selective algorithm and 4 inputs when calculated with the Si LDMOS PA data.

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Augmented Convolutional Neural Network for Behavioral Modeling and Digital Predistortion of Concurrent Multiband Power Amplifiers

Cited by 33 publications

References 30 publications

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Gymnastic Movement Recognition Based on Depth Convolution Neural Network

Selective Algorithm for Expanded Group Method of Data Handling Applied to Power Amplifier Modeling

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