Memory polynomial with binomial reduction in digital pre-distortion for wireless communication systems

One of the biggest power consuming devices in wireless communications system is the Power Amplifier (PA) which amplifies signals non-linearly when operating in real-world systems. The negative effects of PA non-linearity are energy inefficiency, amplitude and phase distortion. The increases in transmission speed in present day communication technology introduces Memory Effects, where signal spreading happens at the PA output, thus causing overhead in signal processing at the receiver side. PA Linearization is therefore required to counter the non-linearity and Memory Effects. Digital Pre-distortion (DPD) is one of the outstanding PA Linearization methods in terms of its strengths in implementation simplicity, bandwidth, efficiency, flexibility and cost. DPD pre-distorts the input signal, using an inversed model function of the PA. Modelling of the PA is therefore vital in DPD, where the Memory Polynomial Method (MP) is used to model the PA with memory effects. In this paper, the MP method is improved in Memory Polynomial using Binomial Reduction method (MPB-imag-2k). The method is simulated using a modelled ZVE-8G Power Amplifier and sampled 4G (LTE) signals. It was found MPB-imag-2k is capable of achieving comparable anti-scattering/anti-distortion in MP for non-linearity order of 3, memory depth of 3 and pre-amplifier gain of 2

Finding Structural Information of RF Power Amplifiers using an Orthogonal Non-Parametric Kernel Smoothing Estimator

A non-parametric technique for modeling the behavior of power amplifiers is presented. The proposed technique relies on the principles of density estimation using the kernel method and is suited for use in power amplifier modeling. The proposed methodology transforms the input domain into an orthogonal memory domain. In this domain, non-parametric static functions are discovered using the kernel estimator. These orthogonal, non-parametric functions can be fitted with any desired mathematical structure, thus facilitating its implementation. Furthermore, due to the orthogonality, the non-parametric functions can be analyzed and discarded individually, which simplifies pruning basis functions and provides a tradeoff between complexity and performance. The results show that the methodology can be employed to model power amplifiers, therein yielding error performance similar to state-of-the-art parametric models. Furthermore, a parameter-efficient model structure with 6 coefficients was derived for a Doherty power amplifier, therein significantly reducing the deployment's computational complexity. Finally, the methodology can also be well exploited in digital linearization techniques.Comment: Matlab sample code (15 MB): https://dl.dropboxusercontent.com/u/106958743/SampleMatlabKernel.zi

Effect of the Base-band Measurement Setup Errors on DPD Performance and Elimination Procedure

In this study, the effect of base-band measurement setup errors on DPD performance was investigated and a calibration procedure is developed to eliminate the measurement errors. A base-band measurement setup is prepared at laboratory with instruments and then the data which is measured and the deteriorating effect of errors on Digital Predistortion (DPD) linearization performance are investigated. In order to eliminate deteriorating effect of this error a three steps calibration procedure is developed. Before and after calibration application DPD performance is measured. It is showed both in simulation and experimentally that the calibration procedure improved the DPD system linearization performance from 10 dB to 26dB and 13dB to 20dB, respectively

METODOLOGÍA PARA LA IMPLEMENTACIÓN DEL MPM EN VHDL Y LA EMULACIÓN DE AMPLIFICADORES DE POTENCIA EN UNA TARJETA FPGA

ResumenEl presente trabajo muestra el diseño e implementación en VHDL del modelo polinomial con memoria que fue seleccionado para la emulación del comportamiento de amplificadores de potencia con el propósito de proporcionar una plataforma de pruebas y evaluación para el modelado matemático y su posterior uso en pre-distorsión digital. Las mediciones de un amplificador real modelo NXP de 10 W medido a 2 GHz se utilizaron para la obtención del modelo matemático el cual fue implementado en una tarjeta de evaluación y desarrollo DSP-FPGA Altera Stratix III. Además el artículo describe el desarrollo de un conjunto de funciones, para la manipulación de números complejos, necesario para la implementación del modelo. Los resultados muestran un desempeño adecuado del modelo en VHDL el cual es capaz de emular las curvas de distorsión en amplitud y fase AM-AM y AM-PM. Finalmente a modo de validación la implementación se compara con una simulación en Matlab.Palabras Claves: Amplificador de potencia, emulación, FPGA, modelo polinomial con memoria, VHDL. METHODOLOGY FOR THE IMPLEMENTATION OF THE MPM IN VHDL AND THE EMULATION OF POWER AMPLIFIERS IN AN FPGA CARDAbstractThis paper shows the design and implementation in VHDL of the memory polynomial model which was selected for emulating the behavior of power amplifiers with the purpose of providing a test and evaluation test bed for mathematical modeling and its later use in digital predistortion. Measurements of a real power amplifier model NXP 10W at 2 GHz were used for obtaining the mathematical model which was implemented in the DSP-FPGA development kit, Stratix III Edition by Altera. This paper also describes the development of a function set for complex numbers manipulation which is needed for the implementation of the model. Results show a correct performance of the VHDL model which can emulate distortion curves for amplitude and phase AM-AM and AM-PM. Finally a comparison is done between VHDL model and Matlab simulation.Keywords: Emulation, FPGA, Memory polynomial model, Power amplifier, VHDL

Augmented-LSTM and 1D-CNN-LSTM based DPD models for linearization of wideband power amplifiers

Abstract. Artificial Neural Networks (ANNs) have gained popularity in modeling the nonlinear behavior of wideband power amplifiers. Recently, modern researchers have used two types of neural network architectures, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), to model power amplifier behavior and compensate for power amplifier distortion. Each architecture has its own advantages and limitations. In light of these, this study proposes two digital pre-distortion (DPD) models based on LSTM and CNN. The first proposed model is an augmented LSTM model, which effectively reduces distortion in wideband power amplifiers. The measurement results demonstrate that the proposed augmented LSTM model provides better linearization performance than existing state-of-the-art DPDs designed using ANNs. The second proposed model is a 1D-CNN-LSTM model that simplifies the augmented LSTM model by integrating a CNN layer before the LSTM layer. This integration reduces the number of input features to the LSTM layer, resulting in a low-complexity linearization for wideband PAs. The measurement results show that the 1D-CNN-LSTM model provides comparable results to the augmented LSTM model. In summary, this study proposes two novel DPD models based on LSTM and CNN, which effectively reduce distortion and provide low-complexity linearization for wideband PAs. The measurement results demonstrate that both models offer comparable performance to existing state-of-the-art DPDs designed using ANNs

Séries de Volterra com truncamentos independentes para dinâmicas e não linearidades em amplificadores de potência

Orientador: Eduardo Gonçalves de LimaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 24/10/2018Inclui referências: p.61-62Área de concentração: TelecomunicaçõesResumo: A pré-distorção digital (DPD) em banda base é uma solução reconhecida para melhorar a eficiência energética de amplificadores de potência (PAs) para sistemas de comunicação sem fio. A série de Volterra polar completa composta por quatro fatores de truncamento é frequentemente selecionada como o modelo para pré-distorcedor (PD). Este trabalho aborda uma nova abordagem à modelagem de PAs baseada em séries de Volterra para reduzir consideravelmente o número de coeficientes gerados. Técnicas já existentes na literatura foram abordadas concorrentemente e aliadamente, como as séries de Volterra polar completa e reduções unidimensionais e bidimensionais difundidas na literatura. A abordagem proposta é baseada na manipulação dos efeitos e distorções observados no PA dividindo a fase de modelagem do PA em quatro sub-modelos, cada um desses replicando a série de Volterra polar. Com isto, cada efeito presente nos PAs pode ser abordado individulamente. O conjunto resultante de 16 fatores de truncamento é então reduzido para dez após forçar seis deles a serem fixados em seus valores mínimos. Para validar a abordagem proposta, as simulações de Matlab são realizadas usando dados experimentais medidos em um PA GaN em classe AB, estimulado por uma onda portadora de 900 MHz modulada por uma envoltória com largura de banda de 3,84 MHz. Uma comparação entre todos os resultados adquiridos ilustra que a técnica aplicada à série de Volterra pode melhorar a precisão de modelos mais simples, com resultados satisfatórios ao fornecer uma redução de 84% do número de coeficientes sem perder grande precisão de modelagem. Foram investigados os aspectos relativos à estrutura de modelagem, de modo a se orientar a melhor seleção de fatores de truncamento que levam à maior redução no número de parâmetros gerados. Foi constatado também que os efeitos de dependência não linear da fase de entrada devem ser investigados, de modo a se determinar a real necessidade de se modelar tais efeitos em cada cenário. Visto que é o efeito mais custoso de se modelar, a omissão deste sub-modelo pode tornar o esforço computacional muito menor sem prejudicar os ganhos gerados pelos outros sub-modelos. Palavras-chaves: Amplificadores de potência, comunicações sem fio, linearização, modelagem, pré-distorção, Volterra polar.Abstract: Digital Baseband Pre-distortion (DPD) is a recognized solution to improve the energetic efficiency of power amplifiers (PAs) for wireless communication systems. The complete polar Volterra series composed of four truncation factors is often selected as the pre-distorter (PD) model. This work presents a new approach to PA modeling based on Volterra series to reduce considerably the number of generated coefficients. Some techniques found in the literature were addressed concurrently and in addition, such as the complete Volterra polar series and one-dimensional and two-dimensional reductions. The proposed approach is based on manipulating the effects and distortions observed in the PA by dividing the modeling phase of the amplifier into four sub-models, each of which replicates the polar Volterra series. By doing so, each effect present in the power amplifiers can be modeled individually. The resulting set of 16 truncation factors is then reduced to ten after forcing six of them to be set to their minimum values. To validate the proposed approach, the Matlab simulations are performed using experimental data from a GaN class AB PA, stimulated by a carrier wave of 900 MHz modulated by an envelope with a bandwidth of 3.84 MHz. A comparison of all the acquired results illustrates that the technique applied to the Volterra series can improve the accuracy of simpler models with satisfactory results by providing a reduction of 84 % of the number of coefficients without losing great modeling accuracy. The aspects related to the modeling structure were investigated, in order to guide the best selection of truncation factors that leads to the greatest reduction in the number of generated parameters. It was also verified that the nonlinear effects generated by the PA must be investigated, in order to determine the real need to model such effects in each scenario. Since this is the most costly effect of modeling, omission of this sub-model can make the computational effort much smaller without damaging the gains generated by the other sub-models. Key-words: Linearization, modeling, polar Volterra, power amplifiers, pre-distortion, wireless communications

Linealización de amplificadores de radiofrecuencia con redes neuronales

Linealización de Amplificadores de Radiofrecuencia con Redes Neuronales:En está tesis doctoral se aborda la linealización de amplificadores de Radiofrecuencia en profundidad.En primer lugar se lleva a cabo una descripción detallada de los diversos sistemas de linealización de amplificadores de radiofrecuencia existentes en la actualidad.Posteriormente se lleva a cabo una minuciosa descripción de la modulación de telecomunicaciones TETRA, sobre la cual va a implementarse el sistema de linealización del amplificador de radiofrecuencia.A continuación se selecciona la tecnología del amplificador de Radiofrecuencia, llevando a cabo un riguroso análisis de las tres tecnologías más importantes (LDMOS, GaN y GaAs) y demostrando las principales ventajas de la solución escogida.Posteriormente, se implementa un sistema de linealización basado en redes neuronales, capaz de linealizar el amplificador de Radiofrecuencia seleccionado, de forma que se cumplan los estándares de telecomunicaciones internacionales para la modulación TETRA y consiguiendo que la complejidad del sistema sea la menor posible, de cara a poder ser implementado empleando los mínimos recursos computacionales y con el menor coste económico posible.Por último se lleva a cabo la implementación física real de la solución completa en un terminal portátil de telecomunicaciones, obteniendo unos excelentes resultados en cuanto a prestaciones y ahorro económico y de recursos computacionales de esta solución respecto a las existentes en el mercado hasta la fecha.<br /