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Augmented Affine Projection Algorithm Based on Error Nonlinearity and Its Simplified Version

IEEE TRANSACTIONS ON signal processing 2024年 72卷 2767-2782页

作者： Gu, Yanglong Ni, Jingen Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China

The augmented affine projection algorithm (AAPA) has a faster convergence rate than the augmented complex-valued LMS (ACLMS) algorithm for correlated inputs. However, they are both not robust against impulsive interference. This work proposes an improved AAPA based on error nonlinearity, which is implemented with the derivative of the logistic distance metric. To reduce its computational complexity, this work also proposes a simplified version of the improved AAPA using the first-order Taylor approximation of the exponential function that is included in the derivative of logistic distance metric. To study the stochastic behaviors of the improved AAPA and its simplified version, their stability conditions and transient and steady-state performance are analyzed. Simulations are performed in the context of system identification and stereo acoustic echo cancellation to show the merits of the two proposed algorithms and to verify the accuracy of the theory on stability conditions and transient and steady-state performance.

关键词： Adaptive filters Vectors Logistics Measurement signal processing algorithms Filtering algorithms Noise Affine projection algorithm augmented linear model impulsive interference logistic distance metric

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A State-of-the-Art Survey on IRS-NOMA for Integrated Sensing and Communication

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IEEE ACCESS 2024年 12卷 186087-186123页

作者： Sur, Samarendra Nath Singh, Arun Kumar Tran, Huu Q. Vishwakarma, Pradeep Imoize, Agbotiname Lucky Li, Chun-Ta Sikkim Manipal Univ Sikkim Manipal Inst Technol Dept Elect & Commun Engn Gangtok 737136 Sikkim India Ind Univ Ho Chi Minh City FET Ho Chi Minh City 70000 Vietnam CNIT I-43124 Parma Italy Politecn Torino DET I-10129 Turin Italy Fu Jen Catholic Univ Grad Inst Appl Sci & Engn Bachelors Program Artificial Intelligence & Inform New Taipei City 24205 Taiwan

In recent years, the rapid advancements in wireless communication technologies have necessitated more efficient and integrated systems. An emerging paradigm addressing this need is the integration of Intelligent Reflecting Surfaces (IRS) with Non-Orthogonal Multiple Access (NOMA) for Integrated Sensing and Communication (ISAC). This paper offers a comprehensive survey of the state-of-the-art IRS-NOMA technologies, emphasizing their potential to enhance ISAC systems. We examine the fundamental principles of IRS and NOMA, investigating how their combined integration can markedly improve spectral and energy efficiencies, system capacity, and overall performance. The survey methodically categorizes and reviews recent research contributions, highlighting key innovations, design methodologies, advancements in the sophisticated algorithms, and performance metrics. Furthermore, we address the practical challenges and implementation issues associated with IRS-NOMA in ISAC, such as hardware limitations, signal processing complexities, and security concerns. Additionally, we outline open research directions and future prospects, aiming to stimulate further advancements in this promising field. Also, we highlight the interplay between the IRS assisted ISAC NOMA with the other emerging technologies towards the realization of 6G. Lastly, we present and summarize the lessons learned from this study. This survey serves as an invaluable resource for researchers and practitioners interested in understanding and advancing IRS-NOMA for next-generation integrated sensing and communication systems.

关键词： Integrated sensing and communication NOMA Surveys Interference cancellation Wireless networks signal processing algorithms Mathematical models Location awareness Downlink 6G mobile communication 6G ISAC IRS optimization sensing wireless communication

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A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第4期21卷 912-923页

作者： Wang, Xingbo Li, Shujuan Pun, Chi-Man Guo, Yijing Xu, Feng Gao, Hao Lu, Huimin Nanjing Univ Posts & Commun Coll Automat Nanjing 210049 Peoples R China Nanjing Univ Posts & Commun Coll Artificial Intelligence Nanjing 210049 Peoples R China Hangzhou Zhuoxi Inst Brain & Intelligence Hangzhou 311100 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Southeast Univ Dept Neurol Zhongda Hosp Nanjing 210009 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu 8048550 Japan

Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the early stage of the disease. In this paper, a Parkinson's auxiliary diagnosis algorithm based on a hyperparameter optimization method of deep learning is proposed for the Parkinson's diagnosis. The diagnosis system uses ResNet50 to achieve feature extraction and Parkinson's classification, mainly including speech signal processing part, algorithm improvement part based on Artificial Bee Colony algorithm (ABC) and optimizing the hyperparameters of ResNet50 part. The improved algorithm is called Gbest Dimension Artificial Bee Colony algorithm (GDABC), proposing "Range pruning strategy" which aims at narrowing the scope of search and "Dimension adjustment strategy" which is to adjust gbest dimension by dimension. The accuracy of the diagnosis system in the verification set of Mobile Device Voice Recordings at King's College London (MDVR-CKL) dataset can reach more than 96%. Compared with current Parkinson's sound diagnosis methods and other optimization algorithms, our auxiliary diagnosis system shows better classification performance on the dataset within limited time and resources.

关键词： Feature extraction Deep learning Residual neural networks Neural networks signal processing algorithms Diseases Data models Artificial bee colony algorithm deep learning hyperparameter optimization parkinson's auxiliary speech diagnosis

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Fast Array Ground Penetrating Radar Localization by CNN-Based Optimization Method

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 4663-4673页

作者： Zhou, Changyu Bai, Xu Yi, Li Shah, Munawar Sato, Motoyuki Tong, Xiaohua Tongji Univ Coll Surveying & Geoinformat Shanghai 200070 Peoples R China Harbin Ind Univ Sch Elect & Informat Engn Harbin Peoples R China Osaka Univ Grad Sch Engn Sci Osaka 5650871 Japan Inst Space Technol Dept GNSS Islamabad 44000 Pakistan Tohoku Univ Ctr Northeast Asian Studies Sendai 9808076 Japan Tongji Univ Dept Surveying & Geoinformat Engn Shanghai 200092 Peoples R China

This article presents an optimization-based approach to overcome redundancy arising from the multivariables enumeration process in multiple signal classification (MUSIC). By incorporating Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization, the computational speed of the MUSIC algorithm is significantly improved while maintaining mathematical accuracy. The optimization techniques require reasonable initial values to start the iteration, while for single target imaging purposes, the initial values can be acquired by the boundary between the near field and the far field. To generate suitable initial values for the optimization, we employ a modified convolutional neural network (CNN) to approximate the boundaries between the near and far fields, which vary with array system properties. Besides, the proposed method introduces a method for the Hessian matrix and gradient initialization for the BFGS method. Using simulation results as training samples, the modified CNN successfully establishes boundary approximations. Simulation and experimentation confirm the feasibility of our proposed method, showing its advantages in both accuracy and computation speed compared to existing techniques.

关键词： Multiple signal classification Optimization Training Convolutional neural networks Convolution Surface treatment signal processing algorithms Broyden-Fletcher-Goldfarb-Shanno (BGFS) convolutional neural network (CNN) multiple signal classification (MUSIC) quasi-Newton method

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Fast Iterative Wiener Filter-Based ISAR Imaging and Cross-Range Scaling With Periodically Gapped CPI

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Wang, Yuanyuan Dai, Fengzhou Liu, Qian Liang, Yi Lu, Xiaofei Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Jiuquan Satellite Launch Ctr Jiuquan 732750 Peoples R China

In this article, the problem of inverse synthetic aperture radar (ISAR) high-resolution imaging and cross-range scaling with periodically gapped coherent processing interval (CPI) caused by the radar performing multiple tasks time divisionally is addressed. In the case of periodically gapped CPI, the traditional fast Fourier transform (FFT) based ISAR imaging method is no longer applicable, and sparse reconstruction is an effective approach to solve this problem. Sparse Bayesian learning (SBL) is the most robust and accurate reconstruction method among all sparse reconstruction algorithms, and it is also an implementation of optimal estimation of the random signal model under the minimum mean square error (MMSE) criterion. Based on the fact that the noise variance of radar observation can be estimated with secondary data, and the amplitudes of the target scatterers can be regarded as unknown deterministic variables, we propose another implementation method for the MMSE estimator, i.e., iterative Wiener filter (IWF), for ISAR imaging and cross-range scaling of periodically gapped data. Compared with SBL, the proposed method has the advantages of lower computational complexity, faster convergence, and higher reconstruction accuracy. Further, we design two fast IWF algorithms based on the triangular-circulant low displacement rank decomposition by utilizing the Toeplitz-block-Toeplitz (TBT) structure, in which almost all operations except matrix decomposition can be calculated using FFT, which can efficiently and accurately process two different types of periodically gapped echo data. Finally, the performance of the algorithm was verified with both simulation and measured data.

关键词： Radar imaging Imaging Radar signal processing algorithms Matrix decomposition Matching pursuit algorithms Scattering Cross-range scaling fast algorithms inverse synthetic aperture radar (ISAR) imaging iterative Wiener filter (IWF) periodically gapped coherent processing interval (CPI)

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Sparse Recovery-Based Target Detection for LFMCW Radar: An Estimation-Before-Detection Strategy

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IEEE SENSORS JOURNAL 2024年第16期24卷 25949-25959页

作者： Xu, Xinju Wang, Yan Jin, Benzhou Shen, Yutong Zhang, Xiaofei Wu, Qihui Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China

To suppress the sidelobes of strong targets, conventional radar signal processing techniques typically use data windows, which results in large performance losses for the detection of weak targets. Sparse recovery (SR) algorithms can suppress sidelobes without using data windows. However, these algorithms still face challenges in target detection, such as dealing with grid mismatch and the difficulty in designing a constant false alarm rate (CFAR) detector. To solve the above problems, we propose an estimate-before-detection (EBD) algorithm for linear frequency-modulated continuous wave (LFMCW) radar. First, parameter estimation is implemented before target detection based on the original signal. Remarkably, it has been proved that the fast Fourier transform (FFT) algorithm can be used to perform estimation of target parameters efficiently. Second, leveraging the sparsity of the received signal, the Gaussian generalized approximate message-passing-based low-complexity sparse Bayesian learning (GGAMP-SBL) algorithm is used to reconstruct targets. Then, target detection is executed using the reconstruction results. Numerical results demonstrate that the proposed detector maintains the CFAR property and outperforms the conventional signal processing (CSP)-based algorithm.

关键词： Object detection signal processing algorithms Vectors Radar Radar detection Estimation Detectors Constant false alarm rate (CFAR) estimate-before-detection Gaussian generalized approximate message-passing-based low-complexity sparse Bayesian learning (GGAMP-SBL) sparse recovery (SR) target detection

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A Novel Prior-Based Channel Estimation and Activity Detection in Cell-Free mMTC Systems

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5500-5516页

作者： Rajoriya, Anupama Singh, Nakul Budhiraja, Rohit Chaturvedi, Ajit K. IIT Kanpur Dept Elect Engn Kanpur 208016 India Georgia Inst Technol Atlanta GA 30332 USA

We consider the problem of joint channel estimation (CE) and device activity detection (DAD) in the uplink of a cell-free millimeter wave massive multiple-input multiple output (mMIMO) system for massive machine-type communication. We know that the mMIMO channel is spatially correlated, and is sparse in the angular domain. The correlation and sparsity are captured by tailoring a Gaussian prior. This prior is then used to design a centralized variational Bayesian learning (cVBL) algorithm for CE and DAD. The variational approximation in cVBL algorithm reduces its complexity from cubic to linear in terms of devices. We next propose an asynchronous decentralized VBL (adVBL) algorithm, wherein each AP locally estimates its channel from all the devices. The adVBL algorithm is robust to the AP failures, and its complexity is invariant of the number of APs. The adVBL algorithm is developed by reformulating cVBL updates as global optimization problems, and by deriving their local counterparts using the alternating direction method of multipliers. Through extensive numerical studies, we show that the proposed cVBL and adVBL algorithms i) outperform several existing algorithms;ii) require much less pilot overhead;and iii) estimate large-scale fading, unlike the existing ones.

关键词： Approximation algorithms signal processing algorithms Correlation Channel estimation Antennas Millimeter wave communication Complexity theory Optimization Bayes methods Vectors Decentralized algorithm expectation maximization variational Bayesian learning

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Singular Value Decomposition-Based Adaptive Sampling Approximate Message Passing Net for Sparse-View CT Reconstruction

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2024年第1期16卷 1页

作者： Wu, Zhenhua Xu, Jiafei Yang, Lixia Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China China Elect Technol Grp 38th Res Inst Hefei 230031 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China

Sparse-viewcomputed tomography (CT) imaging is a promising technique for reducing radiation dose and accelerating data acquisition in medical imaging. However, the challenges of handling a reduced number of projection views persist for both iterative estimation and deep neural reconstruction methods. In this paper, to address these challenges, we present a singular value decomposition-based adaptive sampling approximate message passing network (ASAMP-Net) sparse-view CT imaging method. To achieve multiple sparse views projection within a single scene imaging and alleviate the computational burden, our proposed ASAMP-Net method incorporates an adaptive sampling module into the AMP deep unrolling network. This module dynamically adjusts the data samples used during the learning process, making our method highly adaptable to various projection matrices. Moreover, by decomposing the projection matrix into its principal components, our approach identifies the respective contributions of independent structures. We then select the most significant principal components to construct a projection matrix model with increased orthogonality, thereby enhancing reconstruction performance. Extensive experiments on public datasets demonstrate the superiority of our method. Notably, ASAMP-Net handles various sparse projection views with just a single training process, achieving prominent imaging results compared to other methods in the literature.

关键词： Image reconstruction Computed tomography Training Matrix decomposition Imaging Iterative methods signal processing algorithms Adaptive sampling ASAMP-Net deep unfolding image reconstruction sparse-view CT

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Radar signal Sorting via Graph Convolutional Network and Semi-Supervised Learning

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IEEE signal processing LETTERS 2025年 32卷 421-425页

作者： Li, Ziying Fu, Xiongjun Dong, Jian Xie, Min Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Beijing Inst Technol Tangshan Res Inst Tangshan 063015 Hebei Peoples R China

As a key technology in radar reconnaissance systems, radar signal sorting aims to separate multiple radar pulses from an interleaved pulse stream. Supervised signal sorting methods based on deep learning depend on a large volume of training data to optimize model parameters. However, acquiring labeled pulses in practice is challenging. In this letter, a semi-supervised learning (SSL) framework is proposed to address this issue. First, a Self-Organizing Map (SOM) is used to learn the spatial distribution of impulse features, and an anchor graph is constructed based on SOM nodes. A pseudo-label set is then generated using the SOM based on pulse discrepancy information. Finally, a three-layer Weighted Residual Graph Convolutional Network (WRGCN) is designed for signal sorting, with its parameters pre-trained on pseudo-labels and fine-tuned with a limited number of true labels. Experiments on a simulated radar pulse dataset demonstrate that this framework outperforms several existing methods for radar signal sorting with limited labeled pulses.

关键词： Radar Sorting Vectors Self-organizing feature maps Training Spaceborne radar Semisupervised learning Deep learning Training data signal processing algorithms Radar signal sorting graph convolutional network pseudo-labels semi-supervised learning

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Scene Inference Using Saliency Graphs With Trust-Theoretic Semantic Information Encoding

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IEEE signal processing LETTERS 2025年 32卷 256-260页

作者： Meena, Preeti Kumar, Himanshu Yadav, Sandeep Indian Inst Technol Jodhpur Discipline Elect Engn Jodhpur 342030 India

Scene inference refers to the identification of the scene from a given set of scene representations such as images. A saliency graph of a scene contains scene-defining objects along with semantic information between them in the graph representation. Existing methods consider the entire scene with uniform weighting to all semantic information for scene inference, resulting in a suboptimal performance. This letter presents an optimal edge weight estimation using the trust theoretic framework to encode semantic information effectively in saliency graphs. We have utilized the notion of converged global absolute trust in saliency scores of salient objects to compute the weighting of semantic information. Experimental results highlight the efficacy of the proposed method.

关键词： Peer-to-peer computing Semantics Estimation Symmetric matrices Image edge detection signal processing algorithms Inference algorithms Eigenvalues and eigenfunctions Visualization Accuracy Scene inference saliency graph semantic information trust graph P2P

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