scholarly journals A Bivariate Volterra Series Model for the Design of Power Amplifier Digital Predistorters

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5897
Author(s):  
Carlos Crespo-Cadenas ◽  
María J. Madero-Ayora ◽  
Juan A. Becerra
Keyword(s):  
Power Amplifier ◽  
Power Efficiency ◽  
Volterra Series ◽  
Internal State ◽  
Internal Variable ◽  
Wireless Transmitters ◽  
Class Ab ◽  
Knowledge Based ◽  
New Radio

The operation of the power amplifier (PA) in wireless transmitters presents a trade-off between linearity and power efficiency, being more efficient when the device exhibits the highest nonlinearity. Its modeling and linearization performance depend on the quality of the underlying Volterra models that are characterized by the presence of relevant terms amongst the enormous amount of regressors that these models generate. The presence of PA mechanisms that generate an internal state variable motivates the adoption of a bivariate Volterra series perspective with the aim of enhancing modeling capabilities through the inclussion of beneficial terms. In this paper, the conventional Volterra-based models are enhanced by the addition of terms, including cross products of the input signal and the new internal variable. The bivariate versions of the general full Volterra (FV) model and one of its pruned versions, referred to as the circuit-knowledge based Volterra (CKV) model, are derived by considering the signal envelope as the internal variable and applying the proposed methodology to the univariate models. A comparative assessment of the bivariate models versus their conventional counterparts is experimentally performed for the modeling of two PAs driven by a 30 MHz 5G New Radio signal: a class AB PA and a class J PA. The results for the digital predistortion of the class AB PA under a direct learning architecture reveal the benefits in linearization performance produced by the bivariate CKV model structure compared to that of the univariate CKV model.

scholarly journals Comparison of Feature Selection Techniques for Power Amplifier Behavioral Modeling and Digital Predistortion Linearization

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5772
Author(s):  
Abdoul Barry ◽  
Wantao Li ◽  
Juan A. Becerra ◽  
Pere L. Gilabert
Keyword(s):  
Power Amplifier ◽  
Power Efficiency ◽  
Volterra Series ◽  
Matching Pursuit ◽  
Order Reduction ◽  
Orthogonal Matching Pursuit ◽  
Hill Climbing ◽  
Digital Predistortion ◽  
Lipschitz Optimization ◽  
Reduction Techniques

The power amplifier (PA) is the most critical subsystem in terms of linearity and power efficiency. Digital predistortion (DPD) is commonly used to mitigate nonlinearities while the PA operates at levels close to saturation, where the device presents its highest power efficiency. Since the DPD is generally based on Volterra series models, its number of coefficients is high, producing ill-conditioned and over-fitted estimations. Recently, a plethora of techniques have been independently proposed for reducing their dimensionality. This paper is devoted to presenting a fair benchmark of the most relevant order reduction techniques present in the literature categorized by the following: (i) greedy pursuits, including Orthogonal Matching Pursuit (OMP), Doubly Orthogonal Matching Pursuit (DOMP), Subspace Pursuit (SP) and Random Forest (RF); (ii) regularization techniques, including ridge regression and least absolute shrinkage and selection operator (LASSO); (iii) heuristic local search methods, including hill climbing (HC) and dynamic model sizing (DMS); and (iv) global probabilistic optimization algorithms, including simulated annealing (SA), genetic algorithms (GA) and adaptive Lipschitz optimization (adaLIPO). The comparison is carried out with modeling and linearization performance and in terms of runtime. The results show that greedy pursuits, particularly the DOMP, provide the best trade-off between execution time and linearization robustness against dimensionality reduction.

scholarly journals Design of Class D Audio Power Amplifier Suitable to Hearing Aid Devices

2020 ◽  
Vol 9 (3) ◽  
pp. 143-152
Keyword(s):  
Power Amplifier ◽  
Power Efficiency ◽  
Hearing Aid ◽  
Power Level ◽  
Class Ab ◽  
Class A ◽  
Class D Amplifier ◽  
Audio Amplifier ◽  
Class D ◽  
Audio System

An amplifier is an electronic circuit that improves the strength of the signal. Using an amplifier in an audio system is essential to improve the strength of the signal. Based on different applications and specifications different types of amplifiers are used. Generally in an audio system, the input signal is amplified to a minimum required power level and then the speaker is driven by it. Conventionally, the output stage of an audio amplifier uses Class-A or Class-AB operating in a linear transfer region. The power efficiency of the Class-D amplifier has a better output efficiency compared to Class-A and Class-AB amplifiers, and its distortion is lower than that of the Class-C amplifier. This is based on a known fact that the Class D amplifier has a switching action in which the transistors are either completely on or off. As a result, the amplification is achieved with no power dissipation. Hence, the size of the amplifier can be highly reduced and a smaller heat sink is required. This paper focuses on designing a Class-D power amplifier which is suitable for hearing aid devices to deliver 500mW output power and for the THD to be less than 3%. The circuit implementation is done using UMC high voltage 0.18μm technology. This amplifier consists of three stages such as the modulation stage, the driver stage, the bridge stage, and the demodulation stage. Unfortunately, the Class-D power amplifier has inherent non-linear distortion problems, which is more significant than conventional audio power amplifiers. In this thesis, negative feedback is employed to reduce THD. Without feedback, the THD is obtained as above 3%. By employing feedback, the THD was reduced by 1.96dB. This design result is discussed and through to realization; whereupon the effectiveness of each of the implementation is evaluated.

scholarly journals Machine-Learning Assisted Optimisation of Free-Parameters of a Dual-Input Power Amplifier for Wideband Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2831
Author(s):  
Teng Wang ◽  
Wantao Li ◽  
Roberto Quaglia ◽  
Pere L. Gilabert
Keyword(s):  
Power Amplifier ◽  
Power Efficiency ◽  
Average Power ◽  
Long Term Evolution ◽  
Input Power ◽  
Global Optimisation ◽  
Power Ratio ◽  
Term Evolution ◽  
Free Parameters

This paper presents an auto-tuning approach for dual-input power amplifiers using a combination of global optimisation search algorithms and adaptive linearisation in the optimisation of a multiple-input power amplifier. The objective is to exploit the extra degrees of freedom provided by dual-input topologies to enhance the power efficiency figures along wide signal bandwidths and high peak-to-average power ratio values, while being compliant with the linearity requirements. By using heuristic search global optimisation algorithms, such as the simulated annealing or the adaptive Lipschitz Optimisation, it is possible to find the best parameter configuration for PA biasing, signal calibration, and digital predistortion linearisation to help mitigating the inherent trade-off between linearity and power efficiency. Experimental results using a load-modulated balanced amplifier as device-under-test showed that after properly tuning the selected free-parameters it was possible to maximise the power efficiency when considering long-term evolution signals with different bandwidths. For example, a carrier aggregated a long-term evolution signal with up to 200 MHz instantaneous bandwidth and a peak-to-average power ratio greater than 10 dB, and was amplified with a mean output power around 33 dBm and 22.2% of mean power efficiency while meeting the in-band (error vector magnitude lower than 1%) and out-of-band (adjacent channel leakage ratio lower than −45 dBc) linearity requirements.

Power efficiency enhancement analysis of an inverse class D power amplifier for NB-IoT applications

2021 ◽  
Author(s):  
Mehrdad Harifi-Mood ◽  
Abolfazl Bijari ◽  
Hossein Alizadeh ◽  
Mehdi Forouzanfar ◽  
Nabeeh Kandalaft
Keyword(s):  
Power Amplifier ◽  
Power Efficiency ◽  
Efficiency Enhancement ◽  
Iot Applications ◽  
Class D

scholarly journals Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Mingli Wang ◽  
Huikuan Gu ◽  
Jiang Hu ◽  
Jian Liang ◽  
Sisi Xu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Prediction Model ◽  
Volumetric Modulated Arc Therapy ◽  
Model Evolution ◽  
Prediction Ability ◽  
Knowledge Based ◽  
Proposed Model ◽  
Arc Therapy ◽  
Knowledge Based Planning

Abstract Background and purpose To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. Methods and materials The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated. Results The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the Dmean and V18 Gy for kidney (L/R), the Dmean, V30 Gy, and V40 Gy for bladder, rectum, and femoral head (L/R). Conclusion The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.

scholarly journals Shaping Code in Conjunction with DCT for PAPR Reduction in Multicarrier System

2021 ◽  
Author(s):  
Younes Aimer ◽  
Boubakar Seddik Bouazza ◽  
Smail Bachir ◽  
Claude Duvanaud
Keyword(s):  
Power Amplifier ◽  
Power Efficiency ◽  
Average Power ◽  
Nonlinear Behavior ◽  
Signal Amplitude ◽  
Amplitude Distribution ◽  
Papr Reduction ◽  
Radio Link ◽  
Error Vector Magnitude ◽  
Multicarrier System

Abstract Nonlinear behavior and power efficiency of the Power Amplifier (PA) contradictorily depend on the input signal amplitude distribution. The transmitted signal in multi-carrier modulation exhibits high Peak-to-Average Power Ratio (PAPR) and large bandwidths, leading to the degradation of the radio link and additional generation out-of-band interferences, which degrade the quality of the transmission. Practical solutions exist, like a power back-off, but with unacceptable efficiency performances of the transmitter. This paper deals with efficiency and linearity improvement using a new PAPR reduction method based on the combination of Discrete Cosine Transform (DCT) and shaping technique. The main principle is to determine an optimal coding scheme according to a trade-off between coding complexity and performance benefits in the presence of PA non-linearities. Simulation and experimental results in the context of OFDM signal and using a 20W - 3.7GHz Radio-Frequency Power Amplifier (RF-PA) show an improvement on PAPR reduction of about 3.25dB. Also, the communication criteria like BER (Bit Error Rate) and EVM (Error Vector Magnitude) are improved by about one decade and a half and 8%, respectively.

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