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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 57-67页

作者： Sasaki, Takayuki Hayase, Kazuya Kitahara, Masaki Ono, Shunsuke NTT Corp Yokosuka 2390847 Japan Inst Sci Tokyo Yokohama 2260026 Japan

This paper proposes a sparse regularization method with a novel sorted regularization function. Sparse regularization is commonly used to solve underdetermined inverse problems. Traditional sparse regularization functions, such as L1-norm, suffer from problems like amplitude underestimation and vanishing perturbations. The reverse ordered weighted L1-norm (ROWL) addresses these issues but introduces new challenges. These include developing an algorithm grounded in theory, not heuristics, reducing computational complexity, enabling the automatic determination of numerous parameters, and ensuring the number of iterations remains feasible. In this study, we propose a novel sparse regularization function called the reverse sorted sum of squares (RSSS) and then construct an unrolled algorithm that addresses both the aforementioned two problems and these four challenges. The core idea of our proposed method lies in transforming the optimization problem into a difference-of-convex programming problem, for which solutions are known. In experiments, we apply the RSSS regularization method to image deblurring and super-resolution tasks and confirmed its superior performance compared to conventional methods, all with feasible computational complexity.

关键词： Inverse problems Perturbation methods Vectors Computational complexity Programming Convergence signal processing algorithms Optimization Convex functions signal processing Deep unrolling difference-of-convex inverse problem non-convex optimization proximal splitting method sparse regularization

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A Low Complexity Linear Precoding Method for Extremely Large-Scale MIMO Systems

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2025年 6卷 240-255页

作者： Berra, Salah Benchabane, Abderrazak Chakraborty, Sourav Maruta, Kazuki Dinis, Rui Beko, Marko Kasdi Merbah Ouargla Univ Dept Elect & Telecommun Lab LAGE Ouargla 30000 Algeria Univ Lusofona COPELABS P-1749024 Lisbon Portugal Cooch Behar Govt Engn Coll Dept Elect & Commun Engn Cooch Behar 736170 India Tokyo Univ Sci Fac Engn Dept Elect Engn Tokyo 1628601 Japan FCT UNL Inst Telecomunicacoes P-2825515 Caparica Portugal Univ Lisbon Inst Super Tecn P-1649004 Lisbon Portugal

Massive multiple-input multiple-output (MIMO) systems are critical technologies for the next generation of networks. In this field of research, new forms of deployment are emerging, such as extremely large-scale MIMO (XL-MIMO), in which the antenna array at the base station (BS) is of extreme dimensions. As a result, spatial non-stationary features emerge as users view just a section of the antenna array, known as the visibility regions (VRs). The XL-MIMO systems can achieve higher spectral efficiency, improve cell coverage, and provide significantly higher data rates than standard MIMO systems. It is a promising technology for future sixth-generation (6G) networks. However, due to the large number of antennas, linear precoding algorithms such as Zero-Forcing (ZF) and regularized Zero-Forcing (RZF) methods suffer from unacceptable computational complexity, primarily due to the required matrix inversion. This work aims to develop low-complexity precoding techniques for the downlink XL-MIMO system. These low-complexity linear precoding methods are based on Gauss-Seidel (GS) and Successive Over-Relaxation (SOR) techniques, which avoid calculating the complex matrix inversion and lead to stable linear precoding performance. To further enhance linear precoding performance, we incorporate the Chebyshev acceleration method with the SOR and GS methods, referred to as the Cheby-SOR and Cheby-GS methods. As these proposed methods require optimizing parameters, we create a deep unfolded network (DUN) to optimize the algorithm parameters. Our performance results demonstrate that the proposed method significantly reduces computational complexity from to O(K-2), where $K$ represents the number of users. Moreover, our approach outperforms the original algorithms, requiring only a few iterations to achieve the RZF bit error rate (BER) performance.

关键词： Precoding Massive MIMO Downlink Iterative methods Computational complexity signal processing algorithms Chebyshev approximation Interference Vehicular and wireless technologies Artificial neural networks XL-MIMO non-stationary linear precoding Chebychev acceleration low-complexity iterative method deep unfolding

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Non-Stationary Delayed Combinatorial Semi-Bandit With Causally Related Rewards

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 369-384页

作者： Ghoorchian, Saeed Bilaj, Steven Maghsudi, Setareh Ruhr Univ Bochum Fac Elect Engn & Informat Technol D-44801 Bochum Germany Univ Sci & Technol Tehran Iran Univ Darmstadt Darmstadt Germany Tech Univ Berlin Berlin Germany

Sequential decision-making under uncertainty is often associated with long feedback delays. Such delays degrade the performance of the learning agent in identifying a subset of arms with the optimal collective reward in the long run. This problem becomes significantly challenging in a non-stationary environment with structural dependencies amongst the reward distributions associated with the arms. Therefore, besides adapting to delays and environmental changes, learning the causal relations alleviates the adverse effects of feedback delay on the decision-making process. We formalize the described setting as a non-stationary and delayed combinatorial semi-bandit problem with causally related rewards. We model the causal relations by a directed graph in a stationary structural equation model. The agent maximizes the long-term average payoff, defined as a linear function of the base arms' rewards. We develop a policy that learns the structural dependencies from delayed feedback and utilizes that to optimize the decision-making while adapting to drifts. We prove a regret bound for the performance of the proposed algorithm. Besides, we evaluate our method via numerical analysis using synthetic and real-world datasets to detect the regions that contribute the most to the spread of Covid-19 in Italy.

关键词： Delays Decision making Mathematical models Vectors Numerical analysis COVID-19 signal processing algorithms signal processing Uncertainty Synthetic data Combinatorial multi-armed bandit delayed feedback non-stationary environment online learning uncertainty

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ICASSP 2024 Speech signal Improvement Challenge

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 238-246页

作者： Ristea, Nicolae-Catalin Naderi, Babak Saabas, Ando Cutler, Ross Braun, Sebastian Branets, Solomiya Microsoft Corp Redmond WA 98073 USA

The ICASSP 2024 Speech signal Improvement Challenge aims to advance research in enhancing speech signal quality within communication systems. The speech signal quality can be assessed using the SIG metric from ITU-T P.835 and still remains a top issue in audio communication and conferencing systems. For example, in the ICASSP 2023 Deep Noise Suppression Challenge, the improvement in the background and overall quality is impressive, while the speech signal enhancement was not statistically significant. To improve the speech signal the following speech impairment areas must be addressed: coloration, discontinuity, loudness, reverberation, and noise. To this end, we organized ICASSP 2024 Speech signal Improvement Challenge, which marks the second signal-focused challenge, built upon the success of the previous ICASSP 2023 Speech signal Improvement Challenge. A training and test set was provided for the challenge, and the winners were determined using an extended crowdsourced implementation of ITU-T P.804's listening phase and the word accuracy (WAcc) rate. The results show that significant improvement was made across all measured dimensions of speech quality.

关键词： Speech enhancement Reverberation Noise Measurement Distortion Quality assessment Noise reduction Background noise signal processing algorithms Correlation deep learning subjective testing speech quality assessment

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A Resource-Efficient Time-Domain-Based Algorithm to Estimate Respiration Rate From Single-Lead ECG signal

IEEE OPEN JOURNAL OF INSTRUMENTATION AND MEASUREMENT

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IEEE OPEN JOURNAL OF INSTRUMENTATION AND MEASUREMENT 2025年 4卷

作者： Krishnapriya, G. B. Ponnalagu, R. N. Goel, Sanket Birla Inst Technol & Sci Pilani Dept Elect & Elect Engn MEMS Microfluid & Nano Elect Lab Hyderabad Campus Hyderabad 500078 India Birla Inst Technol & Sci Pilani Dept Elect & Elect Engn Hyderabad Campus Hyderabad 500078 India

This study introduces a novel, computationally efficient time-domain (TD) algorithm for accurate breath rate (BR) estimation from single-lead ECG signals, designed for wearable devices. The proposed algorithm uses statistical TD parameters-mean, prominence, and distance (MPD)-to detect valid respiratory peaks in ECG-derived respiration (EDR) signals. The performance of the MPD algorithm was evaluated using two datasets: 1) a benchmark database containing ECG acquired during dynamic activities and 2) a real-time dataset comprising ECG signals from five subjects performing dynamic activities, including standing, jogging, and recovery. Comparative analysis against state-of-the-art TD methods, such as count-orig, zero-crossing detection, peak detection, and adaptive threshold techniques, demonstrates the superiority of MPD in both accuracy and computational efficiency. On the benchmark dataset, MPD achieved a mean absolute error (MAE) of 3.66 bpm and mean absolute percentage error (MAPE) of 23.69%, outperforming the Count-Orig method (MAE = 5.09 bpm, MAPE = 32.76%). For real-time data, MPD further demonstrated robust performance with an MAE of 1.53 bpm and MAPE of 7.25%. The algorithm's design simplicity, combined with its ability to handle spurious peaks and varying signal conditions, makes it particularly suitable for resource-constrained wearable applications. Its high accuracy, low computational demands, and adaptability across activity conditions underscore its potential for continuous, real-time respiratory monitoring in diverse scenarios.

关键词： Electrocardiography Accuracy Heuristic algorithms Estimation Monitoring Real-time systems Wearable devices signal processing algorithms Performance evaluation Biomedical monitoring Bio medical signal processing denoising ECG-derived respiration (EDR) rate ECG signal processing signal processing wearable devices

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Mixed Grover: A Hybrid Version to Improve Grover's Algorithm for Unstructured Database Search

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON QUANTUM ENGINEERING 2025年第1期6卷 1-13页

作者： Piron, Romain Habibie, Muhammad Idham Goursaud, Claire Univ Lyon INSA Lyon Inria CITI EA 3720 F-69007 Lyon France

In this article, we propose a new strategy to exploit Grover's algorithm for unstructured search problems. We first show that running Grover's routine with a reduced number of iterations but allowing several trials presents a complexity advantage while keeping the same success probability. Then, by a theoretical analysis of the performance, we provide a generic procedure to parameterize the number of iterations k within one shot of Grover's algorithm and the maximum number of trials T, given a targeted success pand the size of the database N. At the end, we highlight that this new approach permits to reduce the computational time by at least 10% for p >= 0.999 independently of the size of the database.

关键词： signal processing algorithms Databases Search problems Registers Flyback transformers Costs Approximation algorithms Qubit Optimization Complexity theory Complexity grover's algorithm hybrid quantum computing unstructured search

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An Enhanced Augmented Coprime Array with Low Mutual Coupling and High DOFs

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7676-7687页

作者： Han, Liang Zhao, Yijiu Mei, Sitao Zhao, Xinyi Wang, Houjun Zhang, Bo University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China University of Electronic Science and Technology of China School of Optoelectronic Science and Engineering Chengdu611731 China

Coprime array (CA) has gained significant attention due to its high degrees of freedom (DOFs) and large array aperture. Previous researches have focused on improving coprime arrays, but few studies have successfully combined a large array aperture, low mutual coupling, and high DOFs with the number of array elements. In this paper, we propose a novel semi-symmetrical augmented coprime array (SSACA) that strategically positions sensors by arranging M-subarrays and symmetric N-subarrays. This design reduces redundancy lags, and increases DOFs, continuous DOFs, and array aperture, thereby mitigating mutual coupling effects and improving angular resolution for the direction of arrival (DOA) estimation. We derive closed-form expressions for the DOFs, continuous DOFs, and array aperture of SSACA. Simulation results demonstrate that the proposed SSACA outperforms the existing studies regarding higher DOFs, reduced mutual coupling effects, and lower root mean square error (RMSE) in similar application scenarios, particularly in strong mutual coupling scenarios. © 1967-2012 IEEE.

关键词： Sensor arrays Sensors Mutual coupling Array signal processing Direction-of-arrival estimation Estimation Vectors Apertures Couplings signal processing algorithms

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DOA Estimation by Jointly Exploiting L1-SVD and Enhanced Spatial Smoothing in Coherent Environment

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhang, Jingchao Li, Muheng Bai, Longxin Qiao, Liyan Harbin Inst Technol HIT Dept Automat Test & Control Harbin 150080 Peoples R China

As a sparse-based direction of arrival (DOA) estimation algorithm, the L1-singular value decomposition (SVD) algorithm is widely used to measure the orientation of targets. In real measurements, the coherent environment that often arises due to multipath propagation leads to the deterioration of the noise immunity and estimation accuracy of the L1-SVD algorithm. Although the decoherence of L1-SVD can be enhanced by introducing spatial smoothing after SVD, which is called SS-L1-SVD, the algorithm does not fully utilize the available information in the observed data. In this article, we propose a new method called L1-enhanced spatial smoothing decomposition (ESSD). ESSD combines spatial smoothing with matrix decomposition by utilizing the relationship among the covariance matrix and the left singular matrix and the singular value matrix. ESSD not only improves the decoherence ability of the algorithm but also makes full use of the information in the observed data and reduces the computational complexity, which makes the algorithm more practical than the traditional algorithms in real measurements. In order to further verify the performance of the new algorithm, we not only performed simulation experiments but also designed a physical experimental platform that can be used for DOA estimation and constructed a real coherent environment caused by multipath propagation and performed physical experiments. The results of simulation and physical experiments show that the L1-ESSD algorithm reduces the error by about 1 degrees and the computation time by about 8 s compared with the conventional L1-SVD algorithm.

关键词： Estimation Direction-of-arrival estimation signal processing algorithms Smoothing methods Noise Antenna arrays Accuracy Vectors Classification algorithms Array signal processing Coherent environment direction of arrival (DOA) enhanced spatial smoothing decomposition (ESSD) L1-singular value decomposition (SVD) multipath effect

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RBF-based Weighted Minimum Likelihood Error Entropy Against Multimodal Noise

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IEEE signal processing Letters 2025年 32卷 2040-2044页

作者： Cui, Mingjing Jiang, Yunxiang Zheng, Yunfei Lin, Dongyuan Wang, Shiyuan Southwest University College of Electronic and Information Engineering Chongqing400715 China Changsha Jinwei Integrated Circuit Co. Ltd. Changsha410000 China

The performance of model-based (MB) robust adaptive filters is highly sensitive to specific noise distributions and significantly degrades under complex multi-peak noise conditions. To this end, utilizing the universal function approximation capability and high training efficiency of radial basis function (RBF), this letter proposes a novel RBF neural network-assisted minimum likelihood error entropy (MLEE) criterion. To accurately estimate the gradient contribution of previous estimation errors to the current one, a weighted method is constructed in MLEE to improve its convergence rate, ultimately generating RBF-WMLEE algorithm. Simulations on system identification validate the superiorities of proposed RBF-WMLEE under complex multimodal noise conditions. © 1994-2012 IEEE.

关键词： Noise Estimation error Training signal processing algorithms Probability density function Entropy Filters Steady-state Convergence Artificial neural networks

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Bias Compensation for Kernel Least-Mean-Square algorithms

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2025年第4期9卷

作者： Chien, Ying-Ren Liu, Jin-Ling Lin, En-Ting Qian, Guobing Natl Taipei Univ Technol Dept Elect Engn Taipei 106344 Taiwan Natl Ilan Univ Dept Elect Engn I Lan City 260007 Taiwan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

This letter addresses the challenge of input noise in nonlinear system identification using kernel adaptive filtering (KAF) techniques. Conventional kernel least-mean-square (KLMS) algorithms are susceptible to input noise, which introduces bias into the estimated weights, degrading performance. To mitigate this issue, we propose a bias-compensated KLMS (BC-KLMS) algorithm. By employing a finite-order nonlinear regression model and leveraging Taylor series expansion, we analyze the bias terms generated by input noise and incorporate them into a modified cost function. The resulting BC-KLMS algorithm effectively reduces noise-induced bias, leading to improved accuracy in nonlinear system identification tasks. Simulation results demonstrate that BC-KLMS outperforms traditional KLMS methods, achieving substantial bias compensation even in low signal-to-noise ratio conditions. This approach enhances the robustness of KAFs in real-world applications where input noise is prevalent.

关键词： Noise Vectors Kernel Adaptive filters signal to noise ratio Nonlinear systems signal processing algorithms Adaptation models Noise measurement Cost function Sensor signal processing bias compensation finite-order nonlinear regression model kernel least-mean-square (KLMS)

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