scholarly journals Efficient Low-Complexity Digital Predistortion for Power Amplifier Linearization

2016 ◽  
Vol 6 (3) ◽  
pp. 1096
Author(s):  
Siba Monther Yousif ◽  
Roslina M. Sidek ◽  
Anwer Sabah Mekki ◽  
Nasri Sulaiman ◽  
Pooria Varahram
Keyword(s):  
Power Amplifier ◽  
High Performance ◽  
Low Complexity ◽  
Memory Effects ◽  
Model Complexity ◽  
Model Parameters ◽  
Digital Predistortion ◽  
Nonlinear Memory ◽  
Proposed Model ◽  
Code Division Multiple

<span lang="EN-US">In this paper, a low-complexity model is proposed for linearizing power amplifiers with memory effects using the digital predistortion (DPD) technique. In the proposed model, the linear, low-order nonlinear and high-order nonlinear memory effects are computed separately to provide flexibility in controlling the model parameters so that both high performance and low model complexity can be achieved. The performance of the proposed model is assessed based on experimental measurements of a commercial class AB power amplifier by applying a single-carrier wideband code division multiple access (WCDMA) signal. The linearity performance and the model complexity of the proposed model are compared with the memory polynomial (MP) model and the DPD with single-feedback model. The experimental results show that the proposed model outperforms the latter model by 5 dB in terms of adjacent channel leakage power ratio (ACLR) with comparable complexity. Compared to MP model, the proposed model shows improved ACLR performance by 10.8 dB with a reduction in the complexity by 17% in terms of number of floating-point operations (FLOPs) and 18% in terms of number of model coefficients.</span>

scholarly journals Efficient Low-Complexity Digital Predistortion for Power Amplifier Linearization

2016 ◽  
Vol 6 (3) ◽  
pp. 1096
Author(s):  
Siba Monther Yousif ◽  
Roslina M. Sidek ◽  
Anwer Sabah Mekki ◽  
Nasri Sulaiman ◽  
Pooria Varahram
Keyword(s):  
Power Amplifier ◽  
High Performance ◽  
Low Complexity ◽  
Memory Effects ◽  
Model Complexity ◽  
Model Parameters ◽  
Digital Predistortion ◽  
Nonlinear Memory ◽  
Proposed Model ◽  
Code Division Multiple

<span lang="EN-US">In this paper, a low-complexity model is proposed for linearizing power amplifiers with memory effects using the digital predistortion (DPD) technique. In the proposed model, the linear, low-order nonlinear and high-order nonlinear memory effects are computed separately to provide flexibility in controlling the model parameters so that both high performance and low model complexity can be achieved. The performance of the proposed model is assessed based on experimental measurements of a commercial class AB power amplifier by applying a single-carrier wideband code division multiple access (WCDMA) signal. The linearity performance and the model complexity of the proposed model are compared with the memory polynomial (MP) model and the DPD with single-feedback model. The experimental results show that the proposed model outperforms the latter model by 5 dB in terms of adjacent channel leakage power ratio (ACLR) with comparable complexity. Compared to MP model, the proposed model shows improved ACLR performance by 10.8 dB with a reduction in the complexity by 17% in terms of number of floating-point operations (FLOPs) and 18% in terms of number of model coefficients.</span>

Performance of a behavioral model with long-term memory effects

2013 ◽  
Vol 5 (4) ◽  
pp. 447-452
Author(s):  
Carlos Crespo-Cadenas ◽  
Javier Reina-Tosina ◽  
María J. Madero-Ayora
Keyword(s):  
Code Division Multiple Access ◽  
Multiple Access ◽  
Low Frequency ◽  
Behavioral Model ◽  
Memory Effects ◽  
Long Term Memory ◽  
Proposed Model ◽  
Reduced Complexity ◽  
Code Division Multiple

This paper presents a new behavioral model for power amplifiers that accomplishes the capture of nonlinear low-frequency memory effects with reduced complexity and superior precision. It has been extensively evaluated with a commercial amplifier using wideband code-division multiple-access (WCDMA)-like modulated data with symbol rates in the range of 2 ksym/s to 1 Msym/s, and it is shown that the first dynamic reduction of the proposed model is successfully compared with other highly efficient methods in terms of complexity and generalization capacity.

A High-Performance Hardware Design for Digital Predistortion System

2013 ◽  
Vol 380-384 ◽  
pp. 3346-3349
Author(s):  
Jian Wang ◽  
Zhi Bin Zeng
Keyword(s):  
Power Amplifier ◽  
High Performance ◽  
High Reliability ◽  
Signal Integrity ◽  
Hardware Design ◽  
Digital Predistortion ◽  
Excellent Performance

Digital predistortion (DPD) is a method widely used to compensate the nonlinearity of power amplifier to improve the transmitting signals. The DPD performance, however, depend heavily on the hardware design on signal integrity, EMI and low additional distortion of downconversion circuit. In this paper, a new high-performance hardware solution for DPD is introduced. Engineering tests show that this hardware design characterizes by high reliability and excellent performance to satisfy the requirements of DPD.

scholarly journals A Lightweight Attention-Based CNN Model for Efficient Gait Recognition with Wearable IMU Sensors

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2866
Author(s):  
Haohua Huang ◽  
Pan Zhou ◽  
Ye Li ◽  
Fangmin Sun
Keyword(s):  
Recognition Accuracy ◽  
Recognition Performance ◽  
Gait Recognition ◽  
Wearable Sensors ◽  
Model Complexity ◽  
Model Parameters ◽  
Softmax Classifier ◽  
Proposed Model ◽  
Comparison Results ◽  
Gait Features

Wearable sensors-based gait recognition is an effective method to recognize people’s identity by recognizing the unique way they walk. Recently, the adoption of deep learning networks for gait recognition has achieved significant performance improvement and become a new promising trend. However, most of the existing studies mainly focused on improving the gait recognition accuracy while ignored model complexity, which make them unsuitable for wearable devices. In this study, we proposed a lightweight attention-based Convolutional Neural Networks (CNN) model for wearable gait recognition. Specifically, a four-layer lightweight CNN was first employed to extract gait features. Then, a novel attention module based on contextual encoding information and depthwise separable convolution was designed and integrated into the lightweight CNN to enhance the extracted gait features and simplify the complexity of the model. Finally, the Softmax classifier was used for classification to realize gait recognition. We conducted comprehensive experiments to evaluate the performance of the proposed model on whuGait and OU-ISIR datasets. The effect of the proposed attention mechanisms, different data segmentation methods, and different attention mechanisms on gait recognition performance were studied and analyzed. The comparison results with the existing similar researches in terms of recognition accuracy and number of model parameters shown that our proposed model not only achieved a higher recognition performance but also reduced the model complexity by 86.5% on average.

Sign in / Sign up

Export Citation Format

Share Document