Strong Nonlinear Distortion Research Articles

Optimum Performance Analysis and Receiver Design for OFDM-Based Frequency-Splitting SWIPT With Strong Nonlinear Effects

Multicarrier based frequency splitting simultaneous wireless information and power transmission (FS-SWIPT) signals are very prone to nonlinear effects due to the combination of high-power energy harvesting (EH) subcarriers with low-power data subcarriers. In this paper, we study the impact of nonlinear effects in FS-SWIPT signals, as well as ways to minimize these effects on the data transmission performance. We start by analytically characterizing the impact of nonlinear devices on the signals, showing that the nonlinear effects on data subcarriers are exacerbated by a factor close to the ratio between the power spectral densities of EH and data terms. This suggests that conventional receivers, that treat nonlinear distortion as an additional noise term, can have very poor performance. Then, we present an iterative receiver that iteratively estimates and cancels nonlinear distortion, and is able to have a performance close to the linear case. Finally, we study the optimum performance of FS-SWIPT signals with strong nonlinear distortion levels, showing that, by using optimum receivers, the nonlinear distortion term can be used to improve the performance of FS-SWIPT, even outperforming the linear case.

Nonlinear system identification based on generalized adaptive exponential function link networks model

Extension functions based on the model of the trigonometric functional link networks (TFLN) and the adaptive exponential functional link networks (AEFLN) have been widely applied in nonlinear identification systems. However, these models are lack of cross-terms (multiple input sample and its past samples). This degrades their performance, especially in nonlinear systems containing strong nonlinear distortion. In this paper, we propose a generalized AEFLN model (GAEFLN) for nonlinear system identification. Since the GAEFLN contains sine, exponential, and cross-terms expansion function, its convergence characteristics will be significantly improved. Simulation results based on nonlinear system identification show that the performance of the proposed GAEFLN is superior to those of the TFLN and AEFLN.

A Low Complexity Moving Average Nested GMP Model for Digital Predistortion of Broadband Power Amplifiers

In this paper, a low complexity moving average nested generalized memory polynomial model (MAN-GMP) is proposed for digital predistortion (DPD) of broadband power amplifiers (PAs). As the signal bandwidth increases drastically, the strong nonlinear distortions, especially those induced by the memory effect, are generated from the highly efficient PAs. To compensate for the strong memory effect, a moving average nested envelope memory polynomial (MAN-EMP) model is derived from an accuracy-enhanced GMP model, which offers reduced complexity while suffering from degraded modeling accuracy. The MAN-GMP model is further proposed to improve the modeling accuracy by connecting several memory branches of the MAN-EMP model in parallel. An iterative algorithm is designed to extract the model coefficients efficiently through only one or two iterations. Experimental measurements are carried out on two sub-7 GHz broadband GaN Doherty PAs with up to 200 MHz bandwidth OFDM signals to benchmark the proposed MAN-GMP model against the GMP, the parallel-LUT-MP-EMP (PLUME), the augmented complexity-reduced GMP (ACR-GMP), the generalized twin-nonlinear two-box (GTNTB), and the enhanced Wiener models. The experimental results show that the MAN-GMP model can effectively compensate for the nonlinear distortion of broadband PAs with significant complexity reduction.

A Unified Approach to Uniform Signal Recovery From Nonlinear Observations

Recent advances in quantized compressed sensing and high-dimensional estimation have shown that signal recovery is even feasible under strong nonlinear distortions in the observation process. An important characteristic of associated guarantees is uniformity, i.e., recovery succeeds for an entire class of structured signals with a fixed measurement ensemble. However, despite significant results in various special cases, a general understanding of uniform recovery from nonlinear observations is still missing. This paper develops a unified approach to this problem under the assumption of i.i.d. sub-Gaussian measurement vectors. Our main result shows that a simple least-squares estimator with any convex constraint can serve as a universal recovery strategy, which is outlier robust and does not require explicit knowledge of the underlying nonlinearity. Based on empirical process theory, a key technical novelty is an approximative increment condition that can be implemented for all common types of nonlinear models. This flexibility allows us to apply our approach to a variety of problems in nonlinear compressed sensing and high-dimensional statistics, leading to several new and improved guarantees. Each of these applications is accompanied by a conceptually simple and systematic proof, which does not rely on any deeper properties of the observation model. On the other hand, known local stability properties can be incorporated into our framework in a plug-and-play manner, thereby implying near-optimal error bounds.

An ISI Scrambling Technique for Dynamic Element Matching Current-Steering DACs

The linearity of high-resolution current-steering digital-to-analog converters (DACs) is often limited by inter-symbol interference (ISI). While dynamic element matching (DEM) can be applied to convert a portion of the ISI to uncorrelated noise instead of nonlinear distortion, DEM alone fails to prevent ISI from at least introducing strong second-order nonlinear distortion. This paper addresses this problem by proposing, analyzing, and experimentally demonstrating a low-cost add-on technique, called ISI scrambling, that, in conjunction with DEM, causes a DAC’s ISI to be free of nonlinear distortion. The ISI scrambling technique is demonstrated in a 1-GS/s, 14-bit DEM DAC implemented in 90 nm CMOS technology. The DAC’s measured linearity is in line with the state-of-the-art and its measured output power spectra closely match those predicted by this paper’s theoretical results.

Mixture of Experts Approach for Piecewise Modeling and Linearization of RF Power Amplifiers

Piecewise behavioral models are commonly adopted for modeling and linearization of RF power amplifiers (PAs) that exhibit strong amplitude-dependent nonlinear distortion characteristics, as global polynomial approximations tend to underperform in such scenarios. In this article, we consider a new piecewise model for PAs based on the mixture of experts (ME) approach, which builds on a probabilistic model that allows the different submodels to cooperate—as opposed to operating in an independent fashion that is commonly the case in existing reference methods. We first introduce the ME framework theory while also extend it such that it can be applied to model complex baseband signals and nonlinearities. Then, we show how the ME model allows overcoming some of the intrinsic shortcomings that existing piecewise behavioral models commonly exhibit, which translates into improved modeling accuracy and improved linearization performance. Furthermore, the extension of the ME approach to a tree-structured regression model, referred to as the hierarchical ME model, is also introduced and shown to provide further performance improvements over the basic ME approach. The proposed solutions are validated with extensive RF measurements, covering both PA direct modeling and digital predistortion (DPD)-based linearization, on a gallium nitride (GaN) load-modulated balanced PA, on a GaN Doherty PA, and on a class AB GaN high electron mobility transistor PA, while being compared against several state-of-the-art piecewise methods. The results demonstrate that the ME framework-based models outperform the state of the art.

Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas.

Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest.

Combination of Orthogonal Injections in Impedance Measurements of Grid-Connected Systems

Impedance-based stability criterion has become a popular method in determining the stability of grid-connected systems. Recent studies have presented the utilization of various pseudo-random-sequence (PRS) perturbations for rapidly and accurately obtaining the impedances required for the stability analysis. A major drawback of the PRS is, however, that the signal power is linearly distributed over many harmonic frequencies. As the injection amplitude must be kept small to avoid too strong nonlinear distortions, it becomes challenging to provide enough power to the whole frequency band of interest.This work proposes a novel perturbation that is synthesized by summing up several independently designed orthogonal PRS injections. As the orthogonal sequences do not have power at common frequencies, the resulting combined signal can be tailored to have a specific spectral-power distribution at each frequency band of interest. As a consequence, the system impedances can be accurately measured over a wide frequency band. The performance of the method is verified through experimental measurements of a 2.7 kW grid-connected system, where grid impedance measurements and terminal inverter output admittance measurements are performed.

On the Achievable Performance of Nonlinear MIMO Systems

Pre-processing techniques employed at the transmitter side of multiple-input, multiple-output (MIMO) systems, such as the ones used by MIMO singular value decomposition (SVD) (MIMO-SVD) techniques, can lead to signals with large envelope fluctuations and high peak-to-average power ratio (PAPR). For this reason, we can have amplification difficulties and increased sensitivity to nonlinear distortion effects. In this letter, we consider MIMO-SVD schemes with strong nonlinear distortion effects. It is shown that contrarily to what one could expect, the asymptotic optimum performance of the MIMO system with strong nonlinear distortion effects can be much better than the optimum performance with an ideal, linear transmitter.

Digitally Assisted Analog Interference Cancellation for In-Band Full-Duplex Radios

In this letter, a digitally assisted analog interference cancellation architecture is proposed for the prevailing full-duplex radios, which simultaneously transmit and receive signals on the same carrier frequency. An auxiliary transmit chain is deployed to generate a canceling signal and subtract from the self-interference (SI) signal at the receiver front end to prevent saturating the analog-to-digital converter. To ensure the canceling signal a close approximation of the SI signal, an observation chain is used to extract the transmitter response. Correspondingly, a two-step modeling process is inserted to the auxiliary transmit chain to recover both the nonlinear behavior and the multi-path channel between the transmit and receive chains. Experimental results have validated the superior performance of this architecture, particularly for transmitters of high output power and strong nonlinear distortions.

Digital volume correlation in an environment with intensive salt-and-pepper noise and strong monotonic nonlinear distortion of light intensity

Digital volume correlation in an environment with intensive salt-and-pepper noise and strong monotonic nonlinear distortion of light intensity

On the Optimum Multicarrier Performance With Memoryless Nonlinearities

It is widely recognized that orthogonal frequency division multiplexing (OFDM) signals are very prone to nonlinear distortion effects, which can lead to significant performance degradation. However, recent results have showed that nonlinear distortion effects do not necessarily mean performance degradation and can actually lead to performance improvements relative to conventional linear OFDM schemes. In this paper, we consider the effects of bandpass memoryless nonlinear devices on OFDM signals and study the optimum asymptotic performance for both nondispersive channels and severely time-dispersive channels. We present analytical methods for obtaining the Euclidean distance between two OFDM signals that are subjected to different nonlinear characteristics. These results are then employed for obtaining the average asymptotic gain relative to conventional linear OFDM schemes in nondispersive channels and the gain distribution for severely time-dispersive channels with Rayleigh-distributed fading on the different multipath components. Our analytical results, which are shown to be very accurate, indicate that the optimum detection of OFDM schemes with strong nonlinear distortion effects allows significant gains when compared with conventional linear OFDM schemes.

Optimum and Sub-Optimum Receivers for OFDM Signals with Strong Nonlinear Distortion Effects

In this paper we consider the optimum detection of OFDM (Orthogonal Frequency Division Multiplexing) signals with strong nonlinear distortion effects. It is shown that the optimum performance with strong nonlinear distortion effects is not as bad as one might expect and can even be better than the performance with conventional, linear transmitters. To achieve these excellent performances we should employ receivers able to take advantage of the information associated to transmitted data symbols that is inherent to the nonlinear distortion component, in opposition to traditional OFDM implementations where nonlinear distortion effects are regarded as an undesirable noise-like component. We study the achievable gains of the optimum receiver both analytically and by simulation. Since the complexity of optimum receivers is extremely high when we have nonlinear distortion effects, even for OFDM signals with a small number of subcarriers, we propose several sub-optimum receivers and evaluate their performance. Our sub-optimal receivers allow remarkable performance improvements, being able to reduce significantly the gap between the optimum performance and the performance of typical OFDM receivers.

Loading techniques for OFDM systems with nonlinear distortion effects

ABSTRACTIn this paper, we study the impact of nonlinear distortion effects on adaptive orthogonal frequency division multiplexing systems. An analytical statistical characterisation of the transmitted signals, which is obtained by taking advantage of the Gaussian‐like nature of the orthogonal frequency division multiplexing signals when the number of subcarriers is high, is included. This statistical characterisation is used to evaluate loading algorithms in the presence of strong nonlinear distortion effects and to redefine loading algorithms taking into account nonlinear distortion issues. Copyright © 2011 John Wiley & Sons, Ltd.

Digital back-propagation for spectrally efficient WDM 112 Gbit/s PM m-ary QAM transmission

We report the performance of coherently-detected nine-channel WDM transmission over high dispersion fibers, using polarization multiplexed m-ary quadrature amplitude modulation (m=4, 16, 64, 256) at 112 Gbit/s. Compensation of fiber nonlinearities via digital back-propagation enables up to 10 dB improvement in maximum transmittable power and ~8 dB Qeff improvement which translates to a nine-fold enhancement in transmission reach for PM-256QAM, where the largest improvements are associated with higher-order modulation formats. We further demonstrate that even under strong nonlinear distortion the transmission reach only reduces by a factor of ~2.5 for a 2 unit increase in capacity (log2m) when full band DBP is employed, in proportion to the required back-to-back OSNR.

Frequency-Response Measurement of Switched-Mode Power Supplies in the Presence of Nonlinear Distortions

Switched-mode converters have become extensively used in powering different consumer products. The increased mass production using low-cost components with high parameter variation has set new challenges to designers. Recent studies have shown that frequency-domain characterization yields most useful information about the dynamics and quality of a converter. This has been widely recognized and fast correlation-based techniques have been developed enabling the use of frequency-response measurements both in design and production phases. These techniques are based on a broad-band excitation signal that is injected into a converter and its response is analyzed. Most of the previous papers have used the pseudorandom binary sequence (PRBS) as the excitation. The conventional binary sequence do not, however, suit well for the processes suffering from strong nonlinear distortions. This paper proposes an excitation signal that can be used similarly to the conventional PRBS but provides much more accurate frequency-response estimates. Inverse-repeat binary sequence (IRS) is applied to cancel the effect of even-order nonlinearities and the frequency response is measured by means of spectrum method. The proposed technique is verified with practical measurements.

Iterative Detection of Multicode DS-CDMA Signals With Strong Nonlinear Distortion Effects

Whenever a direct-sequence code-division multiple-access (DS-CDMA) signal is the sum of several components associated with different spreading codes [e.g., the DS-CDMA signal to be transmitted by the base station (BS) in the downlink or any multicode DS-CDMA signal], it has high envelope fluctuations and a high peak-to-mean envelope power ratio (PMEPR), setting strong linearity requirements for the power amplifiers. For this reason, it is desirable to reduce the envelope fluctuations of the transmitted signals. The use of clipping techniques combined with frequency-domain filtering was shown to be an effective way of reducing the envelope fluctuations (and, inherently, the PMEPR) of DS-CDMA signals, while maintaining the spectral occupation of the corresponding conventional DS-CDMA signals. To avoid PMEPR regrowth effects, the clipping and filtering operations can be repeated several times. However, the performance degradation due to nonlinear distortion effects on the transmitted signals can be relatively high, particularly when a very low PMEPR is intended (e.g., when a low clipping level and several iterations are adopted). This can particularly be serious if different powers are assigned to different spreading codes. To avoid significant performance degradation in these situations, we consider an improved receiver where there is an iterative estimation and cancellation of nonlinear distortion effects. Our performance results show that the proposed receiver allows significant performance improvements after just a few iterations, even when we have strong nonlinear distortion effects.

A turbo SDMA receiver for strongly nonlinearly distorted MC-CDMA signals

This paper considers the use of space division multiple access (SDMA) techniques for the uplink of multi-carrier code division multiple access (MCCDMA) systems, where the transmitted signals face strong nonlinear distortion effects. The signal transmitted by each mobile terminal (MT) is submitted to a nonlinear operation consisting of a clipping device followed by a frequency-domain filtering operation, so as to reduce the envelope fluctuations and peak-to-mean envelope power ratio (PMEPR) while maintaining the spectral occupation of conventional MC-CDMA signals. At the base station, an iterative receiver employing multiple antennas jointly performs turbo multi-user detection and the estimation and cancellation of nonlinear distortion effects. Performance results show that the proposed receiver structures can achieve good performances that are very close to those obtained with linear transmitters, even for high system load and/or when a very low-PMEPR MC-CDMA transmission is intended for each MT.

A new nonlinear multi-filter detection concept for the high density magnetic recording channel

In this work, a new approach to detection in high density magnetic recording is presented. The basic detector structure is a bank of parallel sub-detectors which are specialized in different channel states. The individual adaptation of the sub-detectors to the channel properties is done by a learning algorithm so that no explicit channel model is required. On both simulated and experimental data it is shown that the proposed detection scheme has excellent performance in the presence of strong nonlinear distortion and signal dependent noise. At user densities of 3.5 and 4.0 using RLL(1,7) encoding the new scheme outperforms multi-level decision feedback equalization (MDFE) detection by a factor of up to 500 in error rate. The structure is well suited for hardware implementation.

Atomic structure of the alpha -Al2O3(0001)( sqrt 31 x sqrt 31 )R+/-9 degrees reconstruction.

The projected atomic structure of the $\ensuremath{\alpha}\ensuremath{-}{\mathrm{Al}}_{2}{\mathrm{O}}_{3}(0001)(\sqrt{31}\ifmmode\times\else\texttimes\fi{}\sqrt{31})R\ifmmode\pm\else\textpm\fi{}9\ifmmode^\circ\else\textdegree\fi{}$ reconstruction has been analyzed by means of the grazing incidence x-ray diffraction technique, which is not limited by the surface insulating character. It consists of two Al planes whose structure is close to that of metallic Al(111). This overlayer is rotationally reconstructed, is commensurate with the substrate, and displays strong nonlinear static distortion waves. This layer, oxygen depleted, explains why the surface properties are dramatically changed.