Pruned Volterra Models with Memory Effects for Nonlinear Power Amplifiers

In this paper, an efficient method to characterize electrical memory effects arising from the interaction between the static nonlinearity and the frequency response is proposed using a multi-tone measurement. A transmitter system with nonlinear power amplifier (PA) is modeled with two box Wiener structure that consists of a static Saleh model and a linear filter. A nonlinear least square method with a multi-tone measurement is used to extract frequency response and nonlinear characteristics. The proposed method is verified with a simulation and experiment with a 10W class AB gallium-nitride (GaN) HEMT PA.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents a quantitative metric for memory effects in power amplifiers (PAs) and applies it to various active device technologies and wireless system contexts. The proposed metric is mathematically founded in the dynamic two-tone distortion response, has a clear physical meaning in the important field of PA linearization and can be easily evaluated from either harmonic balance simulations or measurement data gathered in a microwave laboratory. In addition, a memoryless PA linearizer, optimum for reducing the integrated intermodulation distortion (IMD) power in the operation bandwidth for a two-tone excitation, is derived, providing a rigorous figure-of-merit of PA linearizability under static IMD compensation. The application of this figure-of-merit is then illustrated for three different PA prototypes based on Si LDMOS, InGaP/GaAs heterojunction bipolar transistors, and GaN high electron-mobility transistors, designed for 900-MHz (global system for mobile communications), 2.1-GHz (wideband code division multiple access), and 3.5-GHz (WiMax) wireless systems, respectively. </para>

The application of digital predistortion in base band signal is an extended method of amplifier linearization to reduce the adjacent channel interference (ACI) in those systems in which a varying envelope modulation scheme like OFDM and MQAM is used. Digital baseband predistortion is a highly cost-effective way to linearize power amplifiers (PAs), but most existing architectures assume that the PA has a memoryless nonlinearity. For wider bandwidth applications such as wideband code-division multiple access (WCDMA) or wideband orthogonal frequency-division multiplexing (W-OFDM), PA memory effects can no longer be ignored. In this paper we proposed a new technique for adaptation of digital predistorter that considers memory effects in power amplifiers. This method is a combination of two techniques, memory polynomial predistortion and slope-dependent method. This new technique is validated by using a 1.9 GHz 60W LDMOS power amplifier and various signals such as 2-carrier and 3-carrier CDMA.

This paper presents a hardware implementation of a digital predistorter (DPD) for linearizing RF power amplifiers (PAs) for wideband applications. The proposed predistortion linearizer is based on a nonlinear auto-regressive moving average (NARMA) structure, which can be derived from the NARMA PA behavioral model and then mapped into a set of scalable lookup tables (LUTs). The linearizer takes advantage of its recursive nature to relax the LUT count needed to compensate memory effects in PAs. Experimental support is provided by the implementation of the proposed NARMA DPD in a field-programmable gate-array device to linearize a 170-W peak power PA, validating the recursive DPD NARMA structure for W-CDMA signals and flexible transmission bandwidth scenarios. To the best of the authors' knowledge, it is the first time that a recursive structure is experimentally validated for DPD purposes. In addition to the results on PA efficiency and linearity, this paper addresses many practical implementation issues related to the use of FPGA in DPD applications, giving an original insight on actual prototyping scenarios. Finally, this study discusses the possibility of further enhancing the overall efficiency by degrading the PA operation mode, provided that DPD may be unavoidable due to the impact of memory effects.

Power amplifiers (PAs) are inherently nonlinear devices and are used in all transmitters of a communications equipment. To improve the performance of telecommunication channels, digital baseband predistortion is employed to compensate for PA nonlinearities. But most existing architectures base on the memoryless PA model. For wideband signal transmission, PA memory effects can no longer be ignored and memoryless predistortion has poor performance. In this paper, a predistortion scheme based on the indirect learning architecture is proposed and analyzed with application to the wideband wireless transmitter. Then the performance of overall transmitter system is verified by simulation results. The measurement shows that, when inputting a single carrier WCDMA signal with 5 MHz bandwidth, good AM-AM and AM-PM performance and roughly 20 dB improvement of out-of-distortion at an offset frequency of 5 MHz were achieved, after adding the proposed predistortion method.

Power amplifier (PA) nonlinearity plays a crucial role in determining the performance of radio frequency (RF) communication systems. Specifically, PA nonlinearity causes the amplitude-phase modulation (APM) constellation points to get distorted, which results in performance degradation. In this paper, we study the effect of PA nonlinearity on the bit error performance of spatial modulation MIMO (SM-MIMO) systems. When the PA distortion parameters are perfectly known at the receiver, the optimal detection rule is the one which minimizes the maximum-likelihood (ML) cost over the distorted constellation. For the case when the receiver has no knowledge of PA distortion parameters, we propose a receiver compensation technique which involves estimating the points of the distorted APM constellation based on training and performing detection using the estimated constellation. Simulation results show that, for SM-MIMO with 16-QAM, the proposed scheme achieves almost the same bit error performance as that achieved using the perfect knowledge of the PA parameters. Also, the proposed scheme is found to perform within 2–4 dB of the performance achieved using an ideal (linear) PA even at low values of input backoff.

Power Amplifier (PA) is a very important component in communication systems which consume a significant fraction of the total energy and is mostly nonlinear. There is an inverse relationship between the PA efficiency and its linearity. Nonlinear PAs are desirable from an efficiency point of view. The price paid for higher efficiency is that it causes growth in spectrum and causes adjacent channel interference. It also causes in-band distortion, which degrades the bit error rate (BER) performance. Transmission systems, like orthogonal frequency-division multiplexing (OFDM), has tolerance to inter-symbol interference and good spectral efficiency but are subjected to nonlinearities of power amplifiers, due to their high peak-to-average power ratio (PAPR), which occurs because of large fluctuations in their signal envelopes. To reduce the spectral regrowth and to reduce BER, PA linearization is necessary. In this paper, Peak clipping, random phase shifting, selected mapping (SLM) and dummy sequence insertion (DSI) methods have been presented to reduce the PAPR and consequently the effect of nonlinearity and to improve the spectral and energy efficiency of PA.

Memory effects exhibited in RF power amplifiers (PA) have been identified as a major source of performance degradation in modern digital communication systems. While advanced amplifier linearization schemes, such as a digital predistortion (DPD) and envelope elimination restoration, are being used to improve efficiency and linearity in modern digital communication systems, they can also expose communication networks to unwanted emissions from memory effects if not carefully designed and tested. This paper gives a brief description of origins of memory effects in RF power amplifiers. Available methods to quantify memory effects caused by such as parasitic elements are reviewed. Finally a new method based on digital phosphor display technology (DPX) on Real Time Spectrum Analyzers for detecting the presence the memory effects is introduced and its unique capabilities to perform time-correlated measurement results are presented.

Radio frequency power amplifier (PA) is an important part of the transmitter system, which can drive numerous output devices. However, the nonlinear characteristics of PA will cause serious harmonic interference, which leads to electromagnetic interference (EMI) problems. In this article, the nonlinear characteristics and the memory effect of PA are analyzed. The strong nonlinearity region and the weak nonlinearity region are divided according to the strength of the nonlinearity. For the strong nonlinearity, an encoder–decoder-based (E-D-based) artificial neural network model is proposed to predict the harmonic interference of PA. To promote the prediction of high-order harmonics when the input signal is small, a multilayer perceptron model is used for the weak nonlinearity region. The models can effectively predict the first five harmonics of PA, in which the mean absolute error of the fundamental wave is about 0.1 dB, the one of the second-order and the third-order harmonics is about 0.5 dB. Since transfer learning (TL) can simplify the training of the model based on the similarity of different tasks, TL based on model transfer is used to predict the harmonic interference of other PAs according to the existing models. The amount of data required for the modeling of PA can be greatly reduced and the accuracy of prediction can be guaranteed by applying TL. Ultimately, the proposed method can predict the harmonic interference rapidly and accurately according to the known excitation signal so that corresponding measures can be taken to avoid the influence of radiated spurious emission on the use of the sensitive receiving devices in the same electromagnetic environment.

Joint waveform and beamforming design is demonstrated as an effective approach to improve the wireless energy transfer (WET) performance. Meanwhile, the emerging technique of reconfigurable holographic surface (RHS) without the half-wavelength limitation is able to achieve a higher spatial gain. Hence, the RHS-assisted WET system is designed to enhance WET performance by optimizing the waveform and beamforming. However, the ideal linear power amplifier is always conceived, while the nonlinearity of the power amplifier is ignored. In this paper, a more practical nonlinear power amplifier (NPA)-based RHS-assisted WET system is proposed, where the energy harvesting (EH) model by considering the channel aging effect is derived to adapt to the time-varying channel. In order to improve the WET performance, a predistortion technique is adopted to tackle the nonlinear input-output characteristic of NPAs, while the waveform and beamforming are jointly optimized. Simulation results demonstrate that the RHS outperforms the phased array benchmark having the same size on the WET performance, when the NPAs work in the unsaturated region. Furthermore, the impact of the NPAs on the WET performance by considering channel aging effect is demonstrated, while the waveform design is invalid when the NPAs work near the saturation region.

Network densification is essential to enhance the areal capacity of future wireless networks, for which the user-centric network is a promising solution. In the perspective of network scalability, user association and cooperative beamforming (BF) among the base stations (BSs) should be designed elaborately to manage strong interference from nearby BSs. However, practical systems with hardware impairments including nonlinearity of power amplifiers may disturb these techniques and make the network difficult to be scalable. The nonlinearity of power amplifiers affects not only radio frequency (RF)-level signal but also performance according to the number of user equipments (UEs), resulting in necessity of re-designing network-level operations including BS-UE association. This paper proposes a deep learning-based cooperative BF framework for distributed networks with nonlinear power amplifiers. To optimize the cooperative BF for complicated nonlinear systems, we adopt unsupervised learning approach with constraints. To reduce the communication overhead of the network, we propose a novel neural network structure, from which the BSs can perform the cooperative BF in distributed manner. In particular, the information exchange between the central unit and the local BSs is reduced by designing neural network so that the local channel state information are utilized only in the local BSs while the central unit utilizes only covariance matrices. We show that the proposed scheme achieves a higher effective sum rate compared to the baseline schemes by adjusting the user association, BF, and power allocation to control interference and nonlinear distortion with reduced communication overhead.

In this paper, an improved Volterra series with physical knowledge for modeling RF power amplifiers is presented. The parameters of this model can be extracted in digital domain since the model is analyzed based on the envelope signals. To simplify the classical Volterra series, a physical feedback behavioral model is applied to describe power amplifiers. Considering the fading memory effect a new feedback form is presented. The pruned Volterra series is modified depending on the matching network theory to make this model more accurate.

In this paper, a novel method to measure the amplitude and phase of two-tone third-order intermodulation products generated in a high-power amplifier is presented. The method is based on the sampled input and output signals of the amplifier. The presented measurement setup and the associated algorithms for the calculation of the amplitudes and phases are considerably faster and simpler than the current methods. By making use of the sampled input and output signals of the amplifier and the signal processing techniques, the need for a nonlinear reference, a tunable attenuator, and a phase shifter in the existing measurement setups is eliminated, which makes it simple and easy to use. Hence, this is a substantial simplification of the measurement setup compared to what has been reported earlier. The proposed measurement setup is also suitable for fast automated measurements, which is of interest for many applications that are both laboratory and production oriented. In addition, a method to increase the bandwidth of the measured signal is used to overcome the bandwidth limitation set by the Nyquist criteria for sampled systems. Measurements are done on two base-station high-power amplifiers and are found to be in agreement with theory and reference measurements.

Digital predistortion (DPD) is one of the most effective techniques that can compensate for the distortions caused by the nonlinearities and memory effects of power amplifiers (PAs). In this paper,...

The decomposed vector rotation (DVR) model is proposed for digital predistortion (DPD) to compensate for the nonlinearities and memory effects of Power Amplifiers (PA). In this paper, we propose a new neighborhood definition for the DVR model that allows to extend the hill climbing (HC) algorithm to find the optimal structure of DVR model which provides the best compromise between modeling accuracy and complexity. The convergence of the algorithm has been verified and validated using real measurements from Class-AB PA.