Design and implementation of an ETSI-SDR OFDM transmitter with power amplifier linearizer

Satellite radio has attained great popularity because of its wide range of geographical coverage and high signal quality as compared to the terrestrial broadcasts. Most Satellite Digital Radio (SDR) based systems favor multi-carrier transmission schemes, especially, orthogonal frequency division multiplexing (OFDM) transmission because of high data transfer rate and spectral efficiency. It is a challenging task to find a suitable platform that supports fast data rates and superior processing capabilities required for the development and deployment of the new SDR standards. Field programmable gate array (FPGA) devices have the potential to become suitable development platform for such standards. Another challenging factor in SDR systems is the distortion of variable envelope signals used in OFDM transmission by the nonlinear RF power amplifiers (PA) used in the base station transmitters. An attractive option is to use a linearizer that would compensate for the nonlinear effects of the PA. In this research, an OFDM transmitter, according to European Telecommunications Standard Institute (ETSI) SDR Technical Specifications 2007-2008, was designed and implemented on a low-cost Xilinx FPGA platform. A weakly nonlinear PA, operating in the L-band SDR frequency (1.450-1.490GHz), was used for signal transmission. An FPGA-based, low-cost, adaptive linearizer was designed and implemented based on the digital predistortion (DPD) reference design from Xilinx, to correct the distortion effects of the PA on the transmitted signal

Robust Digital Predistortion in Saturation Region of Power Amplifiers

This paper proposes a digital predistortion (DPD) technique to improve linearization performance when the power amplifier (PA) is driven near the saturation region. The PA is a non-linear device in general, and the nonlinear distortion becomes severer as the output power increases. However, the PA’s power efficiency increases as the PA output power increases. The nonlinearity results in spectral regrowth, which leads to adjacent channel interference, and degrades the transmit signal quality. According to our simulation, the linearization performance of DPD is degraded abruptly when the PA operates in its saturation region. To relieve this problem, we propose an improved DPD technique. The proposed technique performs on/off control of the adaptive algorithm based on the magnitude of the transmitted signal. Specifically, the adaptation normally works for small and medium signals while it stops for large signals. Therefore, harmful coefficient updates by saturated signals can be avoided. A computer simulation shows that the proposed method can improve the linearization performance compared with the conventional DPD method in highly driven PAs

Experimental demonstration of digital predistortion for orthogonal frequency-division multiplexing-radio over fibre links near laser resonance

Radio over fibre (RoF), an enabling technology for distribution of wireless broadband service signals through analogue optical links, suffers from non-linear distortion. Digital predistortion has been demonstrated as an effective approach to overcome the RoF non-linearity. However, questions remain as to how the approach performs close to laser resonance, a region of significant dynamic non-linearity, and how resilient the approach is to changes in input signal and link operating conditions. In this work, the performance of a digital predistortion approach is studied for directly modulated orthogonal frequency-division multiplexing RoF links operating from 2.47 to 3.7 GHz. It extends previous works to higher frequencies, and to higher quadrature amplitude modulation (QAM) levels. In addition, the resilience of the predistortion approach to changes in modulation level of QAM schemes, and average power levels are investigated, and a novel predistortion training approach is proposed and demonstrated. Both memoryless and memory polynomial predistorter models, and a simple off-line least-squares-based identification method, are used, with excellent performance improvements demonstrated up to 3.0 GHz

Extreme Learning Machine-Based Receiver for MIMO LED Communications

This work concerns receiver design for light-emitting diode (LED) multiple input multiple output (MIMO) communications where the LED nonlinearity can severely degrade the performance of communications. In this paper, we propose an extreme learning machine (ELM) based receiver to jointly handle the LED nonlinearity and cross-LED interference, and a circulant input weight matrix is employed, which significantly reduces the complexity of the receiver with the fast Fourier transform (FFT). It is demonstrated that the proposed receiver can efficiently handle the LED nonlinearity and cross-LED interference

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

A Digital signal processing-based predistortion technique for reduction of intermodulation distortion

Linearization of power amplifiers has been the topic of many studies, dating back to the work of H. S. Black in the 1920s. For many applications, the well-documented techniques of feedforward and feedback can be used to design low intermodulation distortion (IMD) amplifiers. However, certain applications, including the design of high-power, radio frequency amplifiers, preclude the use of these techniques. The work herein describes an alternative to presently accepted distortion reduction techniques. In-band IM distortion (multi-tone distortion located close in frequency to the desired signal) , is reduced by modifying a baseband input, upconverting this signal to the transmission frequency, then performing the amplification. This allows DSP hardware to be used, resulting in a novel IMD reduction method. The approach presented is unique in that multiple orders of nonlinearity are reduced using DSP technology, at baseband, through a commonly used method of upconversion. Existing work has addressed mostly third-order, analog solutions applied at the frequency of transmission. Theoretical work, simulations, and experimental results are used to describe the technique. Advantages and limitations are discussed, as are areas for future work

Contribution to dimensionality reduction of digital predistorter behavioral models for RF power amplifier linearization

The power efficiency and linearity of radio frequency (RF) power amplifiers (PAs) are critical in wireless communication systems. The main scope of PA designers is to build the RF PAs capable to maintain high efficiency and linearity figures simultaneously. However, these figures are inherently conflicted to each other and system-level solutions based on linearization techniques are required. Digital predistortion (DPD) linearization has become the most widely used solution to mitigate the efficiency versus linearity trade-off. The dimensionality of the DPD model depends on the complexity of the system. It increases significantly in high efficient amplification architectures when considering current wideband and spectrally efficient technologies. Overparametrization may lead to an ill-conditioned least squares (LS) estimation of the DPD coefficients, which is usually solved by employing regularization techniques. However, in order to both reduce the computational complexity and avoid ill-conditioning problems derived from overparametrization, several efforts have been dedicated to investigate dimensionality reduction techniques to reduce the order of the DPD model. This dissertation contributes to the dimensionality reduction of DPD linearizers for RF PAs with emphasis on the identification and adaptation subsystem. In particular, several dynamic model order reduction approaches based on feature extraction techniques are proposed. Thus, the minimum number of relevant DPD coefficients are dynamically selected and estimated in the DPD adaptation subsystem. The number of DPD coefficients is reduced, ensuring a well-conditioned LS estimation while demanding minimum hardware resources. The presented dynamic linearization approaches are evaluated and compared through experimental validation with an envelope tracking PA and a class-J PA The experimental results show similar linearization performance than the conventional LS solution but at lower computational cost.La eficiencia energetica y la linealidad de los amplificadores de potencia (PA) de radiofrecuencia (RF) son fundamentales en los sistemas de comunicacion inalambrica. El principal objetivo a alcanzar en el diserio de amplificadores de radiofrecuencia es lograr simultaneamente elevadas cifras de eficiencia y de linealidad. Sin embargo, estas cifras estan inherentemente en conflicto entre si, y se requieren soluciones a nivel de sistema basadas en tecnicas de linealizacion. La linealizacion mediante predistorsion digital (DPD) se ha convertido en la solucion mas utilizada para mitigar el compromise entre eficiencia y linealidad. La dimension del modelo del predistorsionador DPD depende de la complejidad del sistema, y aumenta significativamente en las arquitecturas de amplificacion de alta eficiencia cuando se consideran los actuales anchos de banda y las tecnologfas espectralmente eficientes. El exceso de parametrizacion puede conducir a una estimacion de los coeficientes DPD, mediante minimos cuadrados (LS), mal condicionada, lo cual generalmente se resuelve empleando tecnicas de regularizacion. Sin embargo, con el fin de reducir la complejidad computacional y evitar dichos problemas de mal acondicionamiento derivados de la sobreparametrizacion, se han dedicado varies esfuerzos para investigar tecnicas de reduccion de dimensionalidad que permitan reducir el orden del modelo del DPD. Esta tesis doctoral contribuye a aportar soluciones para la reduccion de la dimension de los linealizadores DPD para RF PA, centrandose en el subsistema de identificacion y adaptacion. En concrete, se proponen varies enfoques de reduccion de orden del modelo dinamico, basados en tecnicas de extraccion de caracteristicas. El numero minimo de coeficientes DPD relevantes se seleccionan y estiman dinamicamente en el subsistema de adaptacion del DPD, y de este modo la cantidad de coeficientes DPD se reduce, lo cual ademas garantiza una estimacion de LS bien condicionada al tiempo que exige menos recursos de hardware. Las propuestas de linealizacion dinamica presentados en esta tesis se evaluan y comparan mediante validacion experimental con un PA de seguimiento de envolvente y un PA tipo clase J. Los resultados experimentales muestran unos resultados de linealizacion de los PA similares a los obtenidos cuando se em plea la solucion LS convencional, pero con un coste computacional mas reducido.Postprint (published version

A new digital predictive predistorter for behavioral power amplifier linearization

This letter presents a new digital adaptive predistorter (PD) for power amplifier (PA) linearization based on a nonlinear auto-regressive moving average (NARMA) structure. The distinctive characteristic of this PD is its straightforward deduction from the NARMA PA model, without the need of using an indirect learningapproachto identify the PD function.The PD itself presents a NARMA structure, and hence it can be quickly implemented by means of lookup tables. Single and multicarrier modulated signals collected from a three-stage LDMOS class AB PA, with a maximum output power of 48-dBm CW have been used to validate the linearity performance of this new predictive predistorter.Peer Reviewe

Analysis of Internally Bandlimited Multistage Cubic-Term Generators for RF Receivers

Adaptive feedforward error cancellation applied to correct distortion arising from third-order nonlinearities in RF receivers requires low-noise low-power reference cubic nonidealities. Multistage cubic-term generators utilizing cascaded nonlinear operations are ideal in this regard, but the frequency response of the interstage circuitry can introduce errors into the cubing operation. In this paper, an overview of the use of cubic-term generators in receivers relative to other applications is presented. An interstage frequency response plan is presented for a receiver cubic-term generator and is shown to function for arbitrary three-signal third-order intermodulation generation. The noise of such circuits is also considered and is shown to depend on the total incoming signal power across a particular frequency band. Finally, the effects of the interstage group delay are quantified in the context of a relevant communication standard requirement