Characterization of RF Power Amplifier for Narrow and Wide Band Memory Polynomial Implementations

Linearly efficient RF power amplifiers have a tremendous role in wireless communication and radar systems as they lie at the front end of most RF systems. In today’s world of wireless communication, it is not an easy task to design a RF power amplifier that is linearly efficient. There are two main key challenges that one face for making RF power amplifier’s behavior linearly efficient. First is to characterize RF power amplifier’s coefficients smartly. Second is to propose an approach that works on input signal and make its behavior inverse to that of the designed amplifier behavior so that overall response of the system becomes linear. For countering first challenge, most advanced universally accepted algorithms like Memory Polynomial, Generalized Hammerstein, Cross-term Memory Polynomial and Cross-term Hammerstein are implemented to design RF power amplifier models. For countering second challenge, latest DPD algorithms are implemented which make net response of a system linear. The memory models for modelling RF power amplifier are categorized for narrowband and wideband applications. The narrowband power amplifier models include Memory Polynomial and Cross-term Memory Polynomial models whereas wideband power amplifier models include Generalized Hammerstein and Cross-term Hammerstein models. In this paper, various performance indicators like Standard Deviation (SD), Third Order Intercept (TOI), Intermodulation Distortion Products (IMD3), Modulation Error Ratio (MER), Spurious Free Dynamic Range (SFDR) and Error Vector Magnitude (EVM) are used to characterize RF power amplifier for both narrow and wide band applications. The simulation results show that under narrowband applications, Cross-term Memory Polynomial model works best as it has least standard deviation and is also satisfying other performance parameters up to appreciable level with and without DPD algorithm implementation. While for wideband applications, Cross-term Hammerstein model satisfies the performance measuring parameters excellently.

Design, characterisation, and digital linearisation of an ADC analogue front-end for gamma spectroscopy measurements

<p class="Abstract">This work presents the design, experimental characterisation, and digital post-distortion (i.e., digital linearisation) of a MHz-range ADC analogue front-end prototype for a gamma radiation spectrometry system under development at the National Center for Nuclear Research (NCBJ) in Poland. The design accounts for the electrical response of the gamma particle detector in providing signal conditioning and ADC protection against high-voltage spikes due to occasional high-energy cosmic radiation, as well as proper ADC clocking. As the front-end inevitably introduces nonlinear distortion and dynamic effects, a characterisation is performed to quantify the actual performance in terms of Total Harmonic Distortion (THD) and Effective Number of Bits (ENOB). Thus, a digital linearisation based on both static and memory polynomial models is successfully applied by means of post-distortion processing, guaranteeing a substantial improvement in THD and ENOB, and demonstrating the effectiveness of the hardware/software method for gamma radiation spectrometers. </p>

A Weighted Linearization Method for Highly RF-PA Nonlinear Behavior Based on the Compression Region Identification

In this paper, we present an adaptive modeling and linearization algorithm using the weighted memory polynomial model (W-MPM) implemented in a chain involving the indirect learning approach (ILA) as a linearization technique. The main aim of this paper is to offer an alternative to correcting the undesirable effect of spectral regrowth based on modeling and linearization stages, where the 1-dB compression point (P1dB) of a nonlinear device caused by memory effects within a short time is considered. The obtained accuracy is tested for a highly nonlinear behavior power amplifier (PA) properly measured using a field-programmable gate array (FPGA) system. The adaptive modeling stage shows, for the two PAs under test, performances with accuracies of −32.72 dB normalized mean square error (NMSE) using the memory polynomial model (MPM) compared with −38.03 dB NMSE using the W-MPM for the (i) 10 W gallium nitride (GaN) high-electron-mobility transistor (HEMT) radio frequency power amplifier (RF-PA) and of −44.34 dB NMSE based on the MPM and −44.90 dB NMSE using the W-MPM for (ii) a ZHL-42W+ at 2000 MHz. The modeling stage and algorithm are suitably implemented in an FPGA testbed. Furthermore, the methodology for measuring the RF-PA under test is discussed. The whole algorithm is able to adapt both stages due to the flexibility of the W-MPM model. The results prove that the W-MPM requires less coefficients compared with a static model. The error vector magnitude (EVM) is estimated for both the static and adaptive schemes, obtaining a considerable reduction in the transmitter chain. The development of an adaptive stage such as the W-MPM is ideal for digital predistortion (DPD) systems where the devices under test vary their electrical characteristics due to use or aging degradation.

Digital Predistortion (DPD) Algorithm For 5G Applications

The wireless networks of upcoming fifth generation (5G) promises connection speeds and data rates that are one hundred times faster than the existing networks and much improved signal and connection quality. As there is the development of wireless communication systems, the requirement of Power Amplifier’s linearity is hard. The Radio Frequency (RF) power amplifiers that are operating with wideband signals such as WCDMA that is used in UMTS leads to the generation of out-of-band emissions which are referred as “Spectral regrowth”. To improve PA efficiency and linearity, this paper uses Digital Predistortion technique. This paper of DPD in combination with power amplifier using Memory Polynomial Algorithm is designed in the MATLAB/SIMULINK. The designed algorithm was tested with against various test cases and it was observed with high non-linearity and the good performance is seen for the order of 4 and 5 of the memory polynomial and non-linearity coefficients having linear output. Hence by using Memory Polynomial DPD in this paper the output of the previous input and present input is obtained. The advantage of DPD is having a linear power amplifier with less distortion and high non-linearity.

Neural Network DPD for Aggrandizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance

This paper demonstrates an unprecedented novel neural network (NN)-based digital predistortion (DPD) solution to overcome the signal impairments and nonlinearities in Analog Optical fronthauls using radio over fiber (RoF) systems. DPD is realized with Volterra-based procedures that utilize indirect learning architecture (ILA) and direct learning architecture (DLA) that becomes quite complex. The proposed method using NNs evades issues associated with ILA and utilizes an NN to first model the RoF link and then trains an NN-based predistorter by backpropagating through the RoF NN model. Furthermore, the experimental evaluation is carried out for Long Term Evolution 20 MHz 256 quadraturre amplitude modulation (QAM) modulation signal using an 850 nm Single Mode VCSEL and Standard Single Mode Fiber to establish a comparison between the NN-based RoF link and Volterra-based Memory Polynomial and Generalized Memory Polynomial using ILA. The efficacy of the DPD is examined by reporting the Adjacent Channel Power Ratio and Error Vector Magnitude. The experimental findings imply that NN-DPD convincingly learns the RoF nonlinearities which may not suit a Volterra-based model, and hence may offer a favorable trade-off in terms of computational overhead and DPD performance.