Nonlinear Distortion Research Articles

Strong diffraction of nonlinear surface acoustic waves in crystals

Nonlinear distortion and shock formation in planar surface acoustic waves in anisotropic crystals have been modeled without [Hamilton et al., JASA (1999)] and with [Cormack et al., JASA 2022] piezoelectricity. Weak diffraction has been included in the paraxial approximation for nonlinear surface wave beams in isotropic solids [Shull et al., JASA (1995)]. Here, a procedure for including strong diffraction, i.e., without the paraxial approximation, in the model for nonlinear surface waves in crystals is presented, which incorporates the angular spectrum approach described by Kharusi and Farnell (JASA 1970). Anisotropy is defined by expressing the phase speed of a plane wave, and therefore, the magnitude of the corresponding wavenumber, as a function of the direction of propagation. Next, an explicit expression relating the two wavenumber components in the planar surface along which the wave propagates must be obtained as a function of direction, requiring iterative solution of a transcendental relation. The beam is then propagated incrementally away from the source, advancing the angular spectrum in k-space and including nonlinear interactions in the spatial domain to characterize the combined effects of diffraction and nonlinearity. Preliminary results are presented for converging nonlinear surface waves in crystals. [Work supported by IR&D at ARL:UT.]

Clipping Noise Compensation for Overlapped Time Domain Multiplexing toward Low Peak-to-Average Power Ratio.

Overlapped Time Domain Multiplexing (OvTDM) is a high-rate transmission technology that employs the idea of superposition coded modulation (SCM) scheme for signal generation, aiming to achieve maximum channel capacity sharing. Meanwhile, it is also widely considered as a promising technique toward physical layer security. As a main drawback of such system, a high peak-to-average power ratio (PAPR) issue in this system, arising from multi-layer superposition, can be addressed through intentional clipping. However, the detection at the receiver side is vulnerable to nonlinear distortion caused by clipping, which can degrade the performance. To mitigate this distortion, this paper proposed an iterative scheme for estimating and partially canceling clipping distortion at the receiver. We managed to mitigate the impact of clipping noise as much as possible and minimize the cost of optimizing PAPR, thereby improving the transmission performance of OvTDM in the context of amplitude clipping.

DACNN-aided nonlinear equalizer for a probabilistic shaping coherent optical communication system.

The probabilistic shaping (PS) technique is a key technology for fiber optic communication systems to further approach the Shannon limit. To solve the problem that nonlinear equalizers are ineffective for probabilistic shaping optical communication systems with non-uniform distribution, a distribution alignment convolutional neural network (DACNN)-aided nonlinear equalizer is proposed. The approach calibrates the equalizer using the probabilistic shaping prior distribution, which reduces the training complexity and improves the performance of the equalizer simultaneously. Experimental results show nonlinear equalization of 120Gb/s PS 64QAM signals in a 375km transmission scenario. The proposed DACNN equalizer improves the receiver sensitivity by 2.6dB and 1.1dB over the Volterra equalizer and convolutional neural network (CNN) equalizer, respectively. Meanwhile, DACNN converges with fewer training epochs than CNN, which provides great potential for mitigating the nonlinear distortion of PS signals in fiber optic communication systems.

Neural-network-based carrier-less amplitude phase modulated signal generation and end-to-end optimization for fiber-terahertz integrated communication system.

In fiber-terahertz integrated communication systems, nonlinear distortion and inter-symbol interference (ISI) will degrade transmission performance. Pre-compensation is an efficient method to handle the channel distortion as it can avoid noise boosting during channel compensation and reduce receiver side signal processing algorithmic complexity at user-end (UE) considering the asymmetric access scenario. In this paper, we propose and experimentally demonstrate a neural-network (NN)-based carrier-less amplitude phase (CAP) modulated signal generation and end-to-end optimization method for a fiber-terahertz integrated communication system. The CAP signal is generated directly from quadrature amplitude modulation symbols and pre-compensated through a transmitter NN, which allows the receiver to demodulate the signal with simple linear digital signal process (DSP). In generating the CAP signal, the NN based transmitter learns a group of filters, which can generate, up-convert, and pre-compensate the signals. Based on the proposed method, a fiber-terahertz integration access system at 220 GHz is demonstrated and a sensitivity gain of 1.2 dB is achieved at a transmission speed of 50 Gbps and the forward error correction (FEC) bit error rate (BER) threshold of 1 × 10-2 compared with the baseline after 10-km fiber transmission and 1-m wireless delivering.

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization.

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

Modeling of harmonics of amplitude and nonlinear frequency-modulated signals

In vibroseismic data, nonlinear signal distortions called harmonics are usually present. The previously proposed method of separating the signal and its harmonics makes it possible to improve the quality of the initial data, as well as to obtain additional information about the geological structure of the earth interior. One of the limitations of the proposed methodology is its initial development for linear frequency-modulated signals. A new method of prediction of harmonics that allows non-linear frequency modulation as well as amplitude variation is proposed.

Evaluation of User-Centric Cell-Free Massive Multiple-Input Multiple-Output Networks Considering Realistic Channels and Frontend Nonlinear Distortion

Future 6G networks are expected to utilize Massive Multiple-Input Multiple-Output (M-MIMO) and follow a user-centric cell-free (UCCF) architecture. In a UCCF M-MIMO network, the user can be potentially served by multiple surrounding Radio Units (RUs) and Distributed Units (DUs) controlled and coordinated by a single virtualized Centralized Unit (CU). Moreover, in an M-MIMO network, each transmit frontend is equipped with a Power Amplifier (PA), typically with nonlinear characteristics, that can have a significant impact on the throughput achieved by network users. This work evaluates a UCCF M-MIMO network within an advanced system-level simulator considering multicarrier transmission, using Orthogonal Frequency-Division Multiplexing (OFDM), realistic signal-processing steps, e.g., per resource block scheduling, and a nonlinear radio frontend. Moreover, both idealistic independent and identically distributed (i.i.d.) Rayleigh and 3D ray-tracing-based radio channels are evaluated. The results show that under the realistic radio channel, the novel user-centric network architecture can lead to an almost uniform distribution of user throughput and improve the rate of the users characterized by the worst radio conditions by over 3 times in comparison to a classical, network-centric design. At the same time, the nonlinear characteristics of the PA can cause significant degradation of the UCCF M-MIMO network’s performance when operating close to its saturation power.

Structure similarity virtual map generation network for optical and SAR image matching

Introduction: Optical and SAR image matching is one of the fields within multi-sensor imaging and fusion. It is crucial for various applications such as disaster response, environmental monitoring, and urban planning, as it enables comprehensive and accurate analysis by combining the visual information of optical images with the penetrating capability of SAR images. However, the differences in imaging mechanisms between optical and SAR images result in significant nonlinear radiation distortion. Especially for SAR images, which are affected by speckle noises, resulting in low resolution and blurry edge structures, making optical and SAR image matching difficult and challenging. The key to successful matching lies in reducing modal differences and extracting similarity information from the images.Method: In light of this, we propose a structure similarity virtual map generation network (SVGNet) to address the task of optical and SAR image matching. The core innovation of this paper is that we take inspiration from the concept of image generation, to handle the predicament of image matching between different modalities. Firstly, we introduce the Attention U-Net as a generator to decouple and characterize optical images. And then, SAR images are consistently converted into optical images with similar textures and structures. At the same time, using the structural similarity (SSIM) to constrain structural spatial information to improve the quality of generated images. Secondly, a conditional generative adversarial network is employed to further guide the image generation process. By combining synthesized SAR images and their corresponding optical images in a dual channel, we can enhance prior information. This combined data is then fed into the discriminator to determine whether the images are true or false, guiding the generator to optimize feature learning. Finally, we employ least squares loss (LSGAN) to stabilize the training of the generative adversarial network.Results and Discussion: Experiments have demonstrated that the SVGNet proposed in this paper is capable of effectively reducing modal differences, and it increases the matching success rate. Compared to direct image matching, using image generation ideas results in a matching accuracy improvement of more than twice.

Reducing black-box nonlinear state-space models: A real-life case study

A known challenge when building nonlinear models from data is to limit the size of the model in terms of the number of parameters. Especially for complex nonlinear systems, which require a substantial number of state variables, the classical formulation of the nonlinear part (e.g. through a basis expansion) tends to lead to a rapid increase in the model size. In this work, we propose two strategies to counter this effect:1) The introduction of a novel nonlinear-state selection algorithm. The method relies on the non-parametric nonlinear distortion analysis of the Best Linear Approximation framework to identify the state variables which are the most impacted by nonlinearities. Pre-selecting only the most appropriate states when constructing the nonlinear terms results in a considerable reduction of the model size.2) The use of so-called ‘decoupled’ functions directly in the model estimation procedure. While it is known that function decoupling can reduce the model size in a secondary step, we show how a decoupled formulation can be imposed to advantage from the start. The results of this approach are benchmarked with the state-of-the-art a posteriori decoupling technique.Our strategies are demonstrated on real-life data of a multiple-input, multiple-output (MIMO) ground vibration test of an F-16 aircraft, a prime complex and nonlinear dynamic system.

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

Ship-radiated noise is the main basis for ship detection in underwater acoustic environments. Due to the increasing human activity in the ocean, the captured ship noise is usually mixed with or covered by other signals or noise. On the other hand, due to the softening effect of bubbles in the water generated by ships, ship noise undergoes non-negligible nonlinear distortion. To mitigate the nonlinear distortion and separate the target ship noise, blind source separation (BSS) becomes a promising solution. However, underwater acoustic nonlinear models are seldom used in research for nonlinear BSS. This paper is based on the hypothesis that the recovery and separation accuracy can be improved by considering this nonlinear effect in the underwater environment. The purpose of this research is to explore and discover a method with the above advantages. In this paper, a model is used in underwater BSS to describe the nonlinear impact of the softening effect of bubbles on ship noise. To separate the target ship-radiated noise from the nonlinear mixtures, an end-to-end network combining an attention mechanism and bidirectional long short-term memory (Bi-LSTM) recurrent neural network is proposed. Ship noise from the database ShipsEar and line spectrum signals are used in the simulation. The simulation results show that, compared with several recent neural networks used for linear and nonlinear BSS, the proposed scheme has an advantage in terms of the mean square error, correlation coefficient and signal-to-distortion ratio.

Low complexity post-distorter based on extended kernel recursive least squares for visible light communications

In visible light communication (VLC) systems, the data transmission quality could be deteriorated by the nonlinear distortion, which stems from the inherent characteristic of light-emitting diode. In order to eliminate the influence of nonlinearity, reproducing kernel Hilbert space based post distortion technology is an effective solution. As a kind of kernel methods, extended kernel recursive least squares (EX-KRLS) has played a role in providing linear approximation ability and adapting to environment with fast speed. Due to the fact that the implementation of EX-KRLS algorithm relies on constantly growing dictionaries, it leads to high computational complexity. To solve this problem, the approximate linear dependency (ALD) and coherence sparsification criteria (CALD) are introduced in this paper, forming the EX-KRLS-ALD/CALD post-distortions. Simulation results show that compared with the original EX-KRLS, the dictionary size of EX-KRLS-ALD is reduced by 52.6 %, while the size of EX-KRLS-CALD can be reduced by about 77 %, with the same bit error rate performance. The numerical results prove that the proposed strategy meets the expectations in suppressing the dictionary size and reducing the computational costs.

RF classification-based nonlinear distortion mitigation for 120 Gbit/s PAM8-modulated optical interconnects in IM/DD

RF classification-based nonlinear distortion mitigation for 120 Gbit/s PAM8-modulated optical interconnects in IM/DD

Analysis of Nonlinear Distortions of DpHEMT Structures Based on a GaAs/In0.53Ga0.47As Compound with Double-Sided Delta-Doping

Analysis of Nonlinear Distortions of DpHEMT Structures Based on a GaAs/In0.53Ga0.47As Compound with Double-Sided Delta-Doping

GLS–MIFT: A modality invariant feature transform with global-to-local searching

Accurate image matching is the basis for many information fusion-related applications. Conventional methods fail when handling multimodal image pairs with severe nonlinear radiation distortion (NRD) and geometric transformations. To address this problem, we present an effective method, termed modality-invariant feature transform with global-to-local searching (GLS–MIFT). First, we addressed scale changes by constructing the image scale space. Then, we obtained multi-scale, multi-orientation filtering results based on first-order Gaussian steerable filters to exploit their robustness to NRD. Next, we introduced a feature response aggregation model to synthesize the filtering results to generate a feature map, and used it to detect highly repeatable features. Additionally, we designed an adaptive partitioning descriptor to achieve rotation-invariant feature description, involving the following six steps: generation of statistical histograms of multi-orientation filtering values, synthesis of histograms on multiple scales, estimation and updating of the primary direction, determination of the sampling direction, normalization of the feature vector in sub-regions, and finally, obtaining the complete description vector. A convolutional image grouping strategy was used to enhance the rotational invariance of the method. We developed a new feature matcher based on the GLS strategy. Guided by the results of global searching stage, the local searching stage further improved the matching accuracy and reliability of the results. Our experimental results confirmed that GLS–MIFT achieved high-quality matching for a large-scale dataset of 1110 image pairs, with various multimodal image types from the fields of computer vision, medicine, and remote sensing. GLS–MIFT outperformed state-of-the-art methods including SIFT, RIFT, HOWP, OFM, MatchFormer, SemLA and GIFT in qualitative and quantitative evaluations. Our implementation and datasets are available at: https://github.com/Zhongli-Fan/GLS-MIFT.

Inter-channel nonlinearity compensation in long-haul optical fiber communication using overlapped-dense LSTM network: Complexity reduction and visual understanding

The nonlinear distortion resulted from the Kerr effect in optical fibers limits the transmission capacity in long-haul optical fiber communications. In wavelength-division multiplexing (WDM) systems, the inter-channel nonlinear distortion is generally stronger than the intra-channel one, and also more difficult to be compensated, despite that there exist many reports on the nonlinearity compensation methods. Recently, recurrent neural networks have emerged as a potential solution without relying on detailed transmission link information. However, these networks often suffer from high computational complexity and lack interpretability due to their black-box nature. In this paper, we propose a novel overlapped-dense long short-term memory network (OD-LSTM). It can track the correlation of cross-phase-modulation (XPM) and consequently is capable of compensating the inter-channel nonlinear distortion. For a lower complexity, the overlapped-dense structure reduces the hidden units at time steps of different regions, since the correlation of XPM is reduced for the more distant symbols. To evaluate the effectiveness of our proposed scheme, we conduct a numerical simulation on a nine-channel WDM coherent optical transmission over a 1600-km standard single-mode fiber (SSMF) with DP-16QAM at 40 Gbaud. At the optimal optical power of −3 dBm, we observe a 0.52 dB Q-factor gain with the OD-LSTM equalizer, outperforming the digital back-propagation (DBP) with three steps per span and the Co-LSTM regressor. In terms of complexity, our proposed approach achieves a 13.5 % reduction compared to the Co-LSTM classifier, and its complexity is slightly lower than that of the ultralow complexity Co-LSTM regressor. We also discuss the visual understanding of the OD-LSTM, which provides insights into the internal mechanisms of the network.

Robust registration of multi-modal remote sensing images based on multi-dimensional oriented self-similarity features

Registration of multi-modal remote sensing images (MRSI) is crucial for unlocking the full potential of heterogeneous remote sensing imagery. However, achieving accurate registration among MRSI is challenging due to the trade-off between geometric invariance and matching accuracy, caused by differences in signal-to-noise ratio and nonlinear radiometric distortion (NRD) arising from varying imaging mechanisms. To tackle the challenge, this paper proposes a lightweight and hybrid feature-guided registration algorithm for MRSI called the hybrid registration algorithm based on multi-dimensional oriented self-similarity features (MOSS). MOSS leverages the advantages of multi-dimensional oriented self-similarity features to progressively enhance registration performance. In the hybrid feature coarse matching stage, oriented self-similarity features are extracted from MRSI, and their directional information is utilized for feature description to estimate the initial affine transformation. The fine matching under multi-dimensional oriented self-similarity features stage takes the outputs of the coarse matching stage to perform a template-like matching process. To evaluate the performance of MOSS, comprehensive experiments are conducted using six different combinations of MRSI, and seven state-of-the-art registration algorithms are selected for comparison. The experimental results demonstrate that MOSS outperforms the compared methods, with the number of correct matches being at least about 1.6 times higher than the comparison methods. Moreover, MOSS exhibits the lowest root mean square error across all experiments, with an average RMSE of 1.86 pixels, achieving an RMSE within 2 pixels. This highlights its effectiveness in achieving precise alignment and robust registration of MRSI.

Deep Learning-Based SC-FDMA Channel Equalization

It is very challenging to design an effective wireless communication system. That’s because of numerous factors affecting the performance of a typical wireless communication system, such as nonlinear channel distortions and impairments. single carrier frequency division multiple access (SC-FDMA) is a multiple access scheme that is an important part of the long-term evolution (LTE) standard for uplink transmission. An advanced mobile radio system’s multiple access schemes should indeed meet stringent requirements, such as a low bit error rate (BER). In this article, we investigate the equalization problem for nonlinear channel distortions and impairments using deep neural networks (NN). We introduce a novel combined deep neural network channel equalization and symbol detection scheme based on a deep learning (DL) recurrent feedback (RF) long short-term memory (LSTM) neural network to achieve blind equalization and decoding for SC-FDMA systems without knowing the channel state information (CSI). To train the model efficiently, the training data is gathered by simulation, with channel effects and noise treated as a complete black box. CSI and constellation demapping are learned by a deep neural network (DNN) model. Then, the frequency-domain sequences that have been corrupted are implicitly equalized to get the broadcasted signal back. Our specified SC-FDMA system, which uses a quadrature phase-shift keying (QPSK) modulation method and the suggested Deep Learning-based model channel equalizer, performs better than the existing equalizers by an average of 1 to 4 dB at moderate signal-to-noise (SNR) ratios, according to simulation data. A complexity comparison between the proposed and the conventional equalizers was conducted in terms of training time, execution time, and number of operations. On combined channel equalization and symbol detection, the suggested system delivers state-of-the-art performance.

(Why) Do Transparent Hearing Devices Impair Speech Perception in Collocated Noise?

Hearing aids and other hearing devices should provide the user with a benefit, for example, compensate for effects of a hearing loss or cancel undesired sounds. However, wearing hearing devices can also have negative effects on perception, previously demonstrated mostly for spatial hearing, sound quality and the perception of the own voice. When hearing devices are set to transparency, that is, provide no gain and resemble open-ear listening as well as possible, these side effects can be studied in isolation. In the present work, we conducted a series of experiments that are concerned with the effect of transparent hearing devices on speech perception in a collocated speech-in-noise task. In such a situation, listening through a hearing device is not expected to have any negative effect, since both speech and noise undergo identical processing, such that the signal-to-noise ratio at ear is not altered and spatial effects are irrelevant. However, we found a consistent hearing device disadvantage for speech intelligibility and similar trends for rated listening effort. Several hypotheses for the possible origin for this disadvantage were tested by including several different devices, gain settings and stimulus levels. While effects of self-noise and nonlinear distortions were ruled out, the exact reason for a hearing device disadvantage on speech perception is still unclear. However, a significant relation to auditory model predictions demonstrate that the speech intelligibility disadvantage is related to sound quality, and is most probably caused by insufficient equalization, artifacts of frequency-dependent signal processing and processing delays.

Analog CMOS Design in Nanometer Regime

There is no doubt that the short-channel effects have affected the analysis and design of modern analog CMOS integrated circuits significantly. Thus, models that take into account these effects must be adopted in order to obtain more accurate results. In this paper, the AC small-signal low-frequency equivalent circuit of the short-channel MOSFET transistor is developed using an appropriate model. The impact of short-channel effects on the operation of basic building blocks of analog integrated circuits such as basic amplifier configurations, cascode stage, differential amplifier and composite transistor is investigated quantitatively. Additionally, the nonlinear distortion is investigated using the adopted model.

Distortion Analysis and Fault Detection in Weakly Non-linear Analog Circuits Using Simplified Volterra Series Method

In this paper a new method to find the parametric faults in weakly nonlinear analog circuit is proposed. Frequency domain volterra kernels are derived to calculate the test parameters i.e., nonlinear distortions and the cross spectral density. Volterra kernels are sensitive to device parameter and the components used to build the circuit. Device parameter and each component value are varied within the tolerance limit using Monte Carlo simulation. For the fault free circuit maximum and minimum values of the 3rd order harmonic, intermodulation distortion and the cross spectral density are measured. During testing, single and multiple faults are injected and the test parameters are measured. If any one or more test parameters are outside the predetermined bounds the circuit is declared to be faulty. This proposed method offers high fault coverage capability. To validate this method CMOS differential amplifier circuit is taken under consideration.