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Perturbation-Aided Fiber Nonlinearity Compensation in Subcarrier-Multiplexing Systems

JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第19期41卷 6244-6254页

作者： Tang, Du Wu, Zhen Liu, Shuangyue Luo, Jiating Luo, Ji Zheng, Bofang Qiao, Yaojun Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Res Inst China Telecommun Dept Mobile Commun & Terminal Res Guangzhou 510000 Peoples R China Huawei Technol Co Ltd Shenzhen 518129 Peoples R China

A perturbation-aided advanced digital backpropagation (P-ADBP) is proposed for subcarrier-multiplexing (SCM) coherent systems. To improve the nonlinearity compensation (NLC) performance, for the first time, the first-order perturbation analysis is introduced into the conventional ADBP. Based on the fact that the perturbation-based self-subcarrier nonlinearity (PSSN) has a similar form as the cross-subcarrier nonlinearity (CSN), we approximate that PSSN and CSN can be calculated in parallel and the compensation of them can be fused together. This compensation fusion scheme saves the computational complexity effectively by sacrificing only a little performance. Furthermore, to realize the best performance, gradient descent is used to jointly optimize the perturbation coefficients and other necessary parameters in P-ADBP. Verified by 9 x 120-GBaud 1600-km wavelength-division multiplexing SCM systems with dual-polarization 16QAM modulation format, 1-Step P-ADBP provides identical Q(2) improvement as 5-Step ADBP but saves about 72.3% complexity. 5-Step P-ADBP and 10-Step P-ADBP also leapfrog into the NLC level of 10-Step ADBP and 20-Step ADBP, but save about 44.4% and 46.0% complexity, respectively. The proposed algorithm helps achieve an improved performance-complexity trade-off compared to the conventional ADBP and its state-of-the-art modifications.

关键词： Complexity theory Perturbation methods Mathematical models Backpropagation signal processing algorithms Probes Optimization Fiber nonlinearity subcarrier-multiplexing digital backpropagation neural networks fiber optic communication

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A Robust Scattering Center Extraction Method and Its Application in InISAR Imaging

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IEEE SENSORS JOURNAL 2023年第21期23卷 26302-26310页

作者： Kang, Hailong Xu, Liang Zhao, Haojie Li, Jun Guo, Qinghua Zhang, Jun Xidian Univ Hangzhou Inst Technol Hangzhou 311200 Peoples R China Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Univ Wollongong Sch Elect Comp &Telecommun Engn Wollongong NSW 2522 Australia

The extraction of scattering centers is a key step in 3-D imaging of interferometric inverse synthetic aperture radar (InISAR). Due to the influence of background noise and sidelobes, the existing methods often cannot achieve accurate extraction of scattering centers, resulting in outlier or loss of point clouds in the reconstructed 3-D images. To solve this problem, we propose a robust scattering center extraction method based on range-azimuth profile reconstruction. First, a 1-D range profile and a 1-D azimuth profile are reconstructed based on autocorrelation matrix eigenvalue decomposition to suppress the background noise, and then, a threshold is determined based on our proposed threshold updating mechanism to extract the scattering center accurately. The effectiveness and superiority of the proposed method are verified by the simulation results and experimental results based on real data.

关键词： Scattering Image reconstruction Imaging Three-dimensional displays Radar imaging signal processing algorithms Sensors 1-D range profile interferometric inverse synthetic aperture radar (InISAR) scattering center extraction threshold updating

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Actor-Critic Methods for IRS Design in Correlated Channel Environments: A Closer Look Into the Neural Tangent Kernel of the Critic

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 4029-4044页

作者： Evmorfos, Spilios Petropulu, Athina P. Poor, H. Vincent Rutgers State Univ Dept Elect & Comp Engn Piscataway NJ 08854 USA Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA

The article studies the design of an Intelligent Reflecting Surface (IRS) in order to support a Multiple-Input-Single-Output (MISO) communication system operating in a mobile, spatiotemporally correlated channel environment. The design objective is to maximize the expected sum of signal-to-Noise Ratio (SNR) at the receiver over an infinite time horizon. The problem formulation gives rise to a Markov Decision Process (MDP). We propose an actor-critic algorithm for continuous control that accounts for both channel correlations and destination motion by constructing the state of the Reinforcement Learning algorithm to include history of destination positions and IRS phases. To account for the variability of the underlying value function, arising due to the channel variability, we propose to pre-process the input of the critic with a Fourier kernel, which enables stability in the process of neural value approximation. We also examine the use of the destination SNR as a component of the designed MDP state, which constitutes common practice in previous works. We empirically show that, when the channels are spatiotemporally varying, including the SNR in the state representation causes divergence. We provide insight on the aforementioned divergence by demonstrating the effect of the SNR inclusion on the Neural Tangent Kernel of the critic network. Based on our study, we propose a framework for designing actor-critic methods for IRS design and also for more general problems, that is predicated upon sufficient conditions of the critic's Neural Tangent Kernel for convergence under neural value learning.

关键词： signal to noise ratio Spatiotemporal phenomena signal processing algorithms Kernel Correlation Supervised learning Deep learning Intelligent Reflecting Surfaces deep learning reinforcement learning IRS parameter design Neural Tangent Kernels

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Provable General Bounded Denoisers for Snapshot Compressive Imaging With Convergence Guarantee

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2023年 9卷 55-69页

作者： Shi, Baoshun Wang, Yuxin Li, Dan Yanshan Univ Sch Informat Sci & Engn Qinhuang Dao Peoples R China Hebei Key Lab Informat Transmiss & Signal Proc Qinhuang Dao 066004 Peoples R China

In the snapshot compressive imaging (SCI) field, how to explore priors for recovering the original high-dimensional data from its lower-dimensional measurements is a challenge. Recent plug-and-play efforts plugged by deep denoisers have achieved superior performance, and their convergences have been guaranteed under the assumption of bounded denoisers and the condition of diminishing noise levels. However, it is difficult to explicitly prove the bounded properties of existing deep denoisers due to complex network architectures. To address these issues, we propose a novel provable and trainable bounded denoiser using dual tight frames and spatial-variation thresholds. Furthermore, we combine the proposed trainable bounded denoiser with the well-known block matching 3D filtering (BM3D) denoiser for building a plug-and-play SCI framework. Precisely, we formulate a convergent plug-and-play SCI algorithm that can exploit complementary denoiser priors, and plug the two denoisers into it. We explicitly prove that these two denoisers are general bounded denoisers, and further show the convergence of the proposed SCI algorithm. Both simulation (video compressive imaging and hyperspectral compressive imaging) and real data results show that the proposed algorithm can achieve higher-quality reconstructions compared with benchmark SCI algorithms.

关键词： signal processing algorithms Imaging Optimization Convergence Image reconstruction Sensors Noise level Snapshot compressive imaging plug-and-play prior bounded denoiser dual tight frames BM3D

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Downlink Beamforming for Dynamic Metasurface Antennas

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第7期22卷 4745-4755页

作者： Kimaryo, Seraphin F. Lee, Kyungchun Seoul Natl Univ Sci & Technol Dept Elect & Informat Engn Seoul 01811 South Korea Seoul Natl Univ Sci & Technol Res Ctr Elect & Informat Technol Seoul 01811 South Korea

Dynamic metasurface antennas (DMAs) have great potential to be used in the radiator/receptor elements of future wireless transmitters and receivers, replacing conventional metallic antennas. This can be attributed to their unique properties, such as the ability to be reconfigured in real-time and to reduce the radio frequency chains, resulting in low implementation cost. However, the Lorentzian constraint associated with the DMA elements poses a challenge to real-time configuration and limits the application of the DMA. In this study, we propose a DMA-based wireless network, wherein a DMA-equipped base station (BS) communicates with single and multiple users. For the single-user scenario, we develop an optimal algorithm to maximize the signal-to-noise ratio of the user, which provides the weight of each DMA element in closed form. Furthermore, for multiple users, we formulate the weighted sum rate (WSR) problem and employ techniques from the single-user case to develop an efficient alternating optimization algorithm, which optimizes both the transmit precoders and DMA weights, to enhance the WSR of the system under the transmit power constraint of the BS. The numerical results demonstrate the effectiveness of the proposed algorithms in achieving better performance than that of the benchmark schemes.

关键词： Antenna arrays Massive MIMO Antennas Microstrip Optimization signal processing algorithms Metasurfaces Dynamic metasurface antenna (DMA) manifold optimization (MO) precoding massive MIMO

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Adaptive Digital Compensation of Analog Impairments in Frequency Interleaved ADC for Next-Generation High-Speed Communication Receivers

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IEEE ACCESS 2023年 11卷 124855-124868页

作者： Passetti, Leandro Morero, Damian A. Reyes, Benjamin T. Hueda, Mario R. Fdn Fulgor RA-5000 Cordoba Argentina Univ Nacl Cordoba Lab Comunicac Digitales RA-5016 Cordoba Argentina

This paper presents an adaptive background compensation technique designed to address analog impairments in frequency-interleaved analog-to-digital converters. Frequency interleaving has been proposed as a solution to the bandwidth bottleneck of data converters in high-speed digital communication receivers, including those deployed in coherent optical transmission systems. The proposed technique combines an adaptive multiple-input multiple-output equalizer with the well-established backpropagation algorithm commonly utilized in machine learning. Unlike previous proposals, the new algorithm (i) compensates the linear impairments in the analog front-end of the receiver (e.g., mismatches of track-and-hold and trans-impedance amplifier frequency responses, time skews, quadrature imbalance in electrical carriers, etc.), and (ii) maximizes the signal-to-noise ratio at the decision point of the receiver without requiring estimation of the channel parameters. The proposed background compensation scheme is thoroughly investigated in a dual-polarization coherent optical receiver with 16-QAM operating at similar to 200 GBd (i.e., similar to 1.6 Tbps). Numerical results show the excellent performance and high robustness of the new background compensation algorithm. These features, combined with its low implementation complexity, will pave the way for the deployment of commercial transceivers with bandwidths of 100 GHz and beyond.

关键词： Bandwidth signal processing algorithms Calibration signal to noise ratio Optical receivers Equalizers Optical filters Background calibration frequency interleaved ADCs high-speed optical receiver

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Full-Spectrum INFT Algorithm for Dual-Polarization NFDM Transmission

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第10期41卷 3017-3025页

作者： Zhang, Xulun Du, Shucheng Xi, Lixia Wei, Jiacheng Deng, Jiayun Zhang, Ruofan Hu, Meng Zhang, Wenbo Zhang, Xiaoguang Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Beijing Normal Univ Sch Nucl Sci & Technol Beijing 100875 Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China

Nonlinear Fourier transform (NFT)-based nonlinear frequency division multiplexing (NFDM) system has the potential to overcome the limit of fiber nonlinearity. In the NFDM system, the symbol information is modulated onto the nonlinear spectra, which consist of continuous and discrete spectrum. In theory, the dual-polarization (DP) NFDM system with the continuous and discrete spectrum (full spectrum, FS) modulation has the potential to achieve higher spectral efficiency. However, due to the complexity of the NFT and inverse NFT (INFT) numerical algorithms, the DP-FS modulated NFDM system is rarely reported. For the FS-NFDM system, the existing INFT algorithms either only support q-modulation and present poor performance, or have large discrete spectral error as the continuous spectrum energy increases. In this paper, we propose an INFT algorithm design theory. Based the theory, an accurate DP-INFT algorithm is proposed. The algorithm supports not only q-modulation, but also b-modulation with better performance. Employing the proposed INFT algorithm, we demonstrate, for the first time, a 174.5 Gbit/s FS modulated NFDM transmission system with 1440 km.

关键词： Optical fiber theory signal processing algorithms Optical fiber communication Modulation Time-domain analysis Optical fiber polarization Scattering Darboux transformation fiber nonlinearity inverse nonlinear Fourier transform algorithm nonlinear frequency division multiplexing optical fiber communication

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A Deep Denoising Sound Coding Strategy for Cochlear Implants

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2023年第9期70卷 2700-2709页

作者： Gajecki, Tom Zhang, Yichi Nogueira, Waldo Med Univ Hannover & Cluster Excellence Dept Otolaryngol Hearing4all Hannover Germany Med Univ Hannover & Cluster Excellence Dept Otolaryngol Hearing4all D-30625 Hannover Germany

Cochlear implants (CIs) have proven to be successful at restoring the sensation of hearing in people who suffer from profound sensorineural hearing loss. CI users generally achieve good speech understanding in quiet acoustic conditions. However, their ability to understand speech degrades drastically when background interfering noise is present. To address this problem, current CI systems are delivered with front-end speech enhancement modules that can aid the listener in noisy environments. However, these only perform well under certain noisy conditions, leaving quite some room for improvement in more challenging circumstances. In this work, we propose replacing the CI sound coding strategy with a deep neural network (DNN) that performs end-to-end speech denoising by taking the raw audio as input and providing a denoised electrodogram, i.e., the electrical stimulation patterns applied to the electrodes across time. We specifically introduce a DNN that emulates a common CI sound coding strategy, the advanced combination encoder (ACE). We refer to the proposed algorithm as 'Deep ACE'. Deep ACE is designed not only to accurately code the acoustic signals in the same way that ACE would but also to automatically remove unwanted interfering noises, without sacrificing processing latency. The model was optimized using a CI-specific loss function and evaluated using objective measures as well as listening tests in CI participants. Results show that, based on objective measures, the proposed model achieved higher scores when compared to the baseline algorithms. Also, the proposed deep learning-based sound coding strategy gave eight CI users the highest speech intelligibility scores.

关键词： Speech enhancement Speech coding Electrodes signal processing algorithms Wiener filters Noise measurement Decoding Cochlear implants sound coding strategy deep neural networks end-to-end speech enhancement

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Novel Leader-Follower-Based Particle Swarm Optimizer Inspired by Multiagent Systems: Algorithm, Experiments, and Applications

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第3期53卷 1322-1334页

作者： Wang, Chuang Wang, Zidong Han, Qing-Long Han, Fei Dong, Hongli Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163318 Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligen Daqing 163318 Peoples R China Northeast Petr Univ Sanya Offshore Oil & Gas Res Inst Sanya 572025 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia

In this article, as inspired by multiagent systems, a novel leader-follower-based particle swarm optimization (LFPSO) algorithm is presented where the particles are classified into leaders and followers according to their respective roles. The leaders are responsible for searching a wide range of the optimal candidate solutions so as to ensure the diversity of the particle population, and the followers are dedicated to seeking the global-best solution in order to guarantee the convergence of particles. A controller parameter is introduced to fine tune the impact of the leaders on the followers. Owing to the leader-follower mechanism, the proposed LFPSO algorithm not only maintains the diversity of the particle population but also improves the possibility of escaping from the locally optimal solution. It is demonstrated via experimental results that the proposed LFPSO algorithm significantly improves the accuracy and convergence rate of conventional particle swarm optimization algorithms. Furthermore, the LFPSO algorithm is successfully applied to denoise real-time signals in oilfield pipeline network and its superiority over existing denoising algorithms is verified as well.

关键词： signal processing algorithms Statistics Sociology Optimization Heuristic algorithms Particle swarm optimization Convergence Denoising evolutionary computation (EC) leader-follower mechanism (LFM) particle swarm optimization (PSO) variational mode decomposition (VMD)

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Robust Automatic Modulation Classification in Low signal to Noise Ratio

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IEEE ACCESS 2023年 11卷 7860-7872页

作者： An, To Truong Lee, Byung Moo Sejong Univ Seoul South Korea Sejong Univ Dept Intelligent Mechatron Engn & Convergence Engn Seoul 05006 South Korea

In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achieving high classification performance. However, most current DL-AMC methods have limited generalization capabilities under varying noise conditions, especially at low signal-to-noise ratios (SNRs). Therefore, these methods can not be directly applied to practical systems. In this paper, we propose a threshold autoencoder denoiser convolutional neural network (TADCNN), which consists of a threshold autoencoder denoiser (TAD) and a convolutional neural network (CNN). TADs reduce noise power and clean input signals, which are then passed on to CNN for classification. The TAD network generally consists of three components: the batch normalization layer, the autoencoder, and the threshold denoise. The threshold denoise component uses an auto-learning threshold sub-network to compute thresholds automatically. According to experiments, AMC with TAD improved classification accuracy by 70% at low SNR compared with a model without a denoiser. Additionally, our model achieves an average accuracy of 66.64% on the RML2016.10A dataset, which is 6% to 18% higher than the current AMC model.

关键词： Modulation Feature extraction signal to noise ratio Convolutional neural networks Classification algorithms signal processing algorithms Deep learning Automatic modulation classification autoencoder denoiser denoise signal convolutional neural network automatic modulation classification

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