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Laguerre Kernel Adaptive Filter With Arctangent Criterion for Nonlinear System Identification

IEEE signal processing LETTERS 2024年 31卷 2085-2089页

作者： Zhu, Yingying Zhao, Haiquan Christensen, Mads Graesboll Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China Aalborg Univ Audio Anal Lab DK-9000 Aalborg Denmark

Kernel adaptive filters (KAF) have emerged as a prominent method for nonlinear system identification (NSI). However, the KAF becomes computationally intensive as the input signal grows. In complex systems, traditional KAF with a unit time-delay structure may struggle with insufficient control capability. Moreover, KAFs adapted to second-order statistics can be susceptible to non-Gaussian noise. In this letter, we introduce the Laguerre kernel adaptive filter (LKAF) for NSI, using a block-oriented nonlinear model. The LKAF leverages the Laguerre series to approximate the linear block, benefiting from infinite impulse response (IIR) characteristics and a simple feedforward structure. To address non-Gaussian noise, the LKAF employs an arctangent (AT) criterion. This integration leads to the development of the Laguerre kernel arctangent least mean square (L-KATLMS) algorithm and its variations, which utilize random Fourier approximation. Simulation results demonstrate the superiority of our proposed algorithms for NSI.

关键词： Kernel Filters Noise Adaptive filters signal processing algorithms Finite impulse response filters Nonlinear systems Arctangent criterion impulsive noise kernel adaptive filter Laguerre tap-delay line nonlinear system identification random Fourier feature recursive least square

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An Efficient Outlier Rejection Algorithm for Point Cloud Registration

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IEEE signal processing LETTERS 2024年 31卷 1775-1779页

作者： Xiang, Siyi Guo, Shiyi Wei, Hao Liu, Bingxi Zhang, Dabo Liaoning Univ Coll Informat Shenyang 110036 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Peng Cheng Lab Shenzhen Peoples R China

Point cloud registration plays an important role in many applications of computer vision and robotics. Outlier rejection is an essential step in this task. In this letter, we propose an efficient hand-crafted outlier rejection algorithm for point cloud registration. The proposed method includes four modules: seed selection, consensus set construction and sampling, transformation matrix calculation, and hypothesis selection. Particularly, we propose a novel seed selection module based on the addition of elements in the Spatial Consistency (SC) matrix and a new noise suppression strategy in constructing consensus sets. Experimental results on various datasets show that the proposed algorithm presents superior performance over the previous hand-crafted methods.

关键词： Point cloud compression Feature extraction Accuracy signal processing algorithms Noise Noise reduction Matrix decomposition Outlier rejection point cloud registration hand-crafted algorithm 3D feature spatial consistency

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Novel Dictionary Pair Learning Through Locality-Constrained Profiles for the Identification of Chili Pepper Varieties via E-Noses

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IEEE SENSORS JOURNAL 2025年第10期25卷 17530-17540页

作者： Chen, Liang Peng, Guihua Zhang, Changfu Wang, Xueya Chen, Ju Yin, Yong Yan, Jia Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Guizhou Acad Agr Sci Chili Pepper Res Inst Guiyang 550006 Peoples R China Chongqing Key Lab Brain Inspired Comp & Intelligen Chongqing 400715 Peoples R China

In modern society, to make it easier for customers to assess product quality and regularize distribution and supply, almost all crops are sorted and labeled. This sorting and labeling process also facilitates packaging and transportation of products and benefits farmers. As an emerging technology, electronic noses (E-noses) can be applied to food quality control. In this study, we proposed a novel dictionary pair learning (DPL) model named DPL based on locality constraint of profiles (DPL-LCPs), which achieved better performance in the identification of chili pepper varieties using E-noses. This model incorporates profile vectors to define a profile locality constraint term and integrates it with a dictionary reconstruction error term into a unified dictionary learning (DL) model to fully learn the characteristics of samples from various aspects. This approach not only reduces the similarity between dictionary atoms but also avoids the problem of weakening the connections between characteristics of different classes of samples caused by using the block diagonal constraint. In addition, the model utilizes the l(2,1) norm to regularize the analysis dictionary matrix, enhancing the sparsity of features and thereby enhancing the flexibility and robustness of the model. The experimental results show that the proposed model can achieve better classification results and higher stability than other advanced DL models can. With this method, it is possible to reliably identify 13 varieties of chili peppers based on flavor assessment.

关键词： Dictionaries Machine learning Analytical models Accuracy signal processing algorithms Sensors Data models Training Atoms Intelligent sensors Chili pepper dictionary pair learning (DPL) electronic nose (E-nose) locality constraint of profiles (LCPs) variety identification

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Artificial Noise Aided Directional Modulation: A Transmitter Architecture Perspective

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第5期73卷 3046-3060页

作者： Liu, Kanglai Chen, Jiangong Lei, Xia Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China

Directional modulation (DM), as a multi-antenna-based physical layer security (PLS) technology toward millimeter wave (mmWave) scenarios, is capable of guaranteeing effective and secure transmissions by simultaneously performing beamforming and artificial noise (AN). Over the past decade, the DM architecture has changed from a fully analog (FA) one to a fully digital (FD) one. Additionally, the hybrid architecture, which effectively balances both performance and hardware considerations, has unfortunately received limited attention regarding the integration of DM within its framework. In this paper, we intend to give a detailed study of DM technology under the still imperfect FA and more effective hybrid architectures. Specifically, for a single-user (SU) multiple-input single-output (MISO) FA-DM systems, considering the constant modulus constraint (CMC) of phase shifters (PSs) as well as the power allocation issue, we propose a low-complexity DM precoder with closed-form solutions by maximizing the entropy of precoder (EoP). On the other hand, optimization problems are cast and discussed under different RF chain number configurations for SU multiple-input multiple-output (MIMO) hybrid-DM systems. Specifically, as the RF chain count decreases from twice the data stream number to match it, we all use optimization to achieve the corresponding optimal or near-optimal solutions. Finally, simulation results verify our aforementioned analysis and demonstrate the superiority of the proposed FA and hybrid-DM algorithms in terms of secrecy rate and bit error rate (BER) compared to the counterparts when PSs with low resolution are used.

关键词： Radio frequency signal processing algorithms Computer architecture Array signal processing Precoding Resource management Baseband Vectors Modulation Noise Directional modulation hybrid precoding beamforming artificial noise entropy of precoder finite resolution phase shifters

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Spatial Ultra-Sparse Array Formation on LEO Distributed Satellite Cluster: An Enhanced Hybrid Particle Swarm Method

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第2期19卷 447-460页

作者： He, Yuanzhi Yang, Peng Man, Yunying Wang, Changxu Qi, Chengwu 32002 Troops PLA Beijing 100141 Peoples R China Univ Elect & Sci Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China Army Engn Univ PLA Coll Commun Engn Nanjing 210000 Peoples R China

The rapid development of Direct-to-device (D2D) services has put forward higher requirements for the performance of satellite antenna systems. The Spatial Ultra-Sparse Distributed Array (SUSDA) constructed by Distributed Satellite Cluster (DSC) has the characteristics of strong directivity, high flexibility and strong anti-jamming ability, which can better meet the communication requirements in future D2D scenarios. However, the non-uniform arrangement of SUSDA leads to the increase of the side lobe level (SLL) and the decrease of the overall antenna performance. To solve this problem, this paper proposes for the first time a configuration design method for a Low Earth Orbit (LEO) SUSDA capable of supporting D2D services in future 6G scenarios. It constructs a mathematical model related to the configuration design of the LEO SUSDA and provides a rapid prediction of the performance of the SUSDA radiation pattern function based on a probabilistic model. Then, an Enhanced Hybrid Particle Swarm Optimization (EHPSO) algorithm is proposed to solve the configuration design problem, which overcomes the slow convergence problem of traditional HPSO algorithm particularly when the array scale is large. The EHPSO algorithm adapts to the search requirements of different stages by adjusting parameters adaptively. It introduces a single suboptimal particle solution to enhance competition and cooperation among particles and employs a local search strategy to precisely narrow the search domain. Simulation results show that the algorithm can significantly reduce the number of iterations and running time of the algorithm while ensuring computational accuracy, which provides a new solution to the configuration design problem of large-scale LEO SUSDA in the future.

关键词： Satellites Low earth orbit satellites Antenna arrays Satellite antennas Optimization Phased arrays signal processing algorithms Antenna radiation patterns Approximation algorithms Aperture antennas Satellite communication D2D SUSDA EHPSO

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Deep Minimax Classifiers for Imbalanced Datasets With a Small Number of Minority Samples

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第3期19卷 491-506页

作者： Choi, Hansung Seo, Daewon DGIST Elect Engn & Comp Sci Daegu 42988 South Korea

The concept of a minimax classifier is well-established in statistical decision theory, but its implementation via neural networks remains challenging, particularly in scenarios with imbalanced training data having a limited number of samples for minority classes. To address this issue, we propose a novel minimax learning algorithm designed to minimize the risk of worst-performing classes. Our algorithm iterates through two steps: a minimization step that trains the model based on a selected target prior, and a maximization step that updates the target prior towards the adversarial prior for the trained model. In the minimization, we introduce a targeted logit-adjustment loss function that efficiently identifies optimal decision boundaries under the target prior. Moreover, based on a new prior-dependent generalization bound that we obtained, we theoretically prove that our loss function has a better generalization capability than existing loss functions. During the maximization, we refine the target prior by shifting it towards the adversarial prior, depending on the worst-performing classes rather than on per-class risk estimates. Our maximization method is particularly robust in the regime of a small number of samples. Additionally, to adapt to overparameterized neural networks, we partition the entire training dataset into two subsets: one for model training during the minimization step and the other for updating the target prior during the maximization step. Our proposed algorithm has a provable convergence property, and empirical results indicate that our algorithm performs better than or is comparable to existing methods.

关键词： Training Training data Minimization Accuracy signal processing algorithms Neural networks Classification algorithms Feature extraction Data models Decision theory Minimax training imbalanced data adversarial prior

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Field Evaluation of a Softwarized Modem From the Perspective of 5G Cell Search

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第6期24卷 4987-5002页

作者： Lim, Dawoon Jeong, Subin Kim, Bitna Lee, Yelan Shin, Hye Jin Kim, Juyeop Hyundai Mobis Uiwang 16891 South Korea Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea Sookmyung Womens Univ Dept Elect Engn Seoul 04310 South Korea

Modem softwarization, where baseband signals are fully processed using software on a general-purpose CPU, is a promising technology in mobile communications due to its simplicity and flexibility in realizing various features. On the other hand, many still question the effectiveness of a softwarized modem in commercial environments concerning performance and complexity. Motivated by this perspective, this paper presents the design and implementation of a softwarized modem with the specific feature of 5G cell search for field evaluation. Based on the baseline algorithms of 5G cell search in Open Air Interface (OAI), we propose a new software architecture which can efficiently manage a 5G cell search procedure and decompose the overall 5G cell search into sub-algorithms. We also design and implement novel sub-algorithms that enhance the detection of Synchronization signal Blocks (SSBs). Our softwarized modem utilizes dual-rate sampling to significantly reduce computation complexity during timing offset estimation. It also adaptively detects synchronization signals or cell identities based on the presence of inter-cell interference or multi-path fading. The performance evaluation through field experiments concludes that our softwarized modem outperforms the baseline, and the proposed sub-algorithms are effective in enhancing cell search performance. The detection probability and time consumption results for our softwarized modem confirm that it is feasible for commercial uses.

关键词： Modems Computer architecture Microprocessors 5G mobile communication Synchronization signal processing algorithms Symbols Software algorithms OFDM Complexity theory 5G cell search softwarized modem field evaluation synchronization

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ADMM-Based MIMO Equalization Algorithm for SDM Transmission With High MDL Tolerance

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第5期43卷 1427-1439页

作者： Kamiya, Norifumi NEC Corp Ltd Adv Network Res Labs Kawasaki Kanagawa 2118666 Japan

In this paper, we propose a novel multiple-input multiple-output (MIMO) equalization algorithm for optical space division multiplexing (SDM) systems impaired by mode-dependent loss (MDL). The proposed algorithm is a least mean square (LMS)-like algorithm derived on the basis of the alternating direction method of multipliers (ADMM) framework and consists of an iterative filtering process and filter tap update process. The filtering process is implemented in the frequency domain using the overlap-save technique and the filter update process is a gradient descent-based algorithm which tries to minimize the mean squared error of the equalizer output. The computational complexity of the proposed algorithm increases in proportion to the number of iterations and is the same as that of conventional LMS-based MIMO equalization when the number of iterations is one. We evaluate, through simulations, the bit error rate (BER) performance of the proposed algorithm for a coupled multi-core fiber channel model with significant MDL. The simulation results show that increasing the number of iterations improves the BER performance significantly compared to the conventional LMS-based equalization algorithm. The proposed algorithm offers a good tradeoff between BER performance and computational complexity and enhances the MDL tolerance for SDM systems.

关键词： Vectors Symbols signal processing algorithms Bit error rate Constraint optimization Computational complexity Adaptive equalizers Propagation losses Frequency-domain analysis Space division multiplexing Coupled space division multiplexing transmission mode-dependent loss MIMO equalization ADMM error back-propagation frequency domain equalization overlap-save method

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A New Design of Low Hardware Cost and Low Power Programmable FIR Filters

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第4期71卷 2314-2318页

作者： Fu, Zongpei Ye, Wenbin Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China

Programmable Finite Impulse Response (FIR) filters (Proakis et al., 2007) are widely used in various fields such as communication, radio, audio, and signal processing. The Reconfigurable Multiple Constant Multiplication (RMCM) modules are commonly employed to design programmable FIR filters. In this brief, we propose a novel RMCM optimization algorithm to optimize the hardware cost and power consumption of the RMCM module which means lower chip area and lower operating power consumption. In order to reduce the overall search space, a segmented analysis method that divided the global search space into several sub-search spaces is first proposed. In addition, we introduce a novel necessity-first search algorithm for each sub-search space, which can optimize the structures of the multiplexers and shifters in the RMCM module simultaneously. The benchmark filters examples show that the proposed method can reduce the hardware cost and power consumption by at least 13.04% and 2.74% compared with the existing state-of-art methods.

关键词： Optimization Finite impulse response filters Hardware Optimized production technology Adders Costs signal processing algorithms Programmable FIR filter reconfigurable MCM optimization ASIC

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Sign-Enhanced Semidefinite Programming Algorithm and its Application to Independent Component Analysis

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第3期19卷 536-548页

作者： Wang, Dahu Liu, Chang Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100039 Peoples R China

Independent component analysis (ICA) is widely applied in remote sensing signal processing. Among various ICA algorithms, the modified semidefinite programming (MSDP) algorithm stands out. However, the efficacy and safety of MSDP depend on the distribution of data. Our research found that MSDP is better suited for handling data with a super-Gaussian distribution. As real-world data usually exhibit a combination of sub-Gaussian and super-Gaussian distributions, MSDP faces challenges in accurately extracting all independent components (ICs). To solve this problem, we conducted a comprehensive analysis of the MSDP algorithm and introduced an enhanced version, the sign-enhanced MSDP (SMSDP) algorithm. By incorporating the sign function into the projected Hessian matrix, SMSDP enables the algorithm to effectively extract ICs from data characterized by a mixture of sub-Gaussian and super-Gaussian distributions. Furthermore, we provided a detailed comparison with MSDP to illustrate why SMSDP can achieve more accurate eigenpairs. Some experiments have demonstrated the effectiveness of SMSDP. The experiments in blind separation of image/sound, radar clutter removal, and real hyperspectral feature extraction also show the superiority of SMSDP in improving the accuracy of IC extraction.

关键词： Tensors signal processing algorithms Kurtosis Vectors Mathematical models Data mining Feature extraction Hyperspectral imaging Gaussian distribution Semidefinite programming Independent component analysis (ICA) feature extraction blind image separation radar clutter removal

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