Many real-world time series, such as electricity demand data, biomedical signals, and mechanical vibration signals, exhibit complex trends, encompass multiple seasonal (or periodic) components, and are prone to noise contamination. Existing decomposition methods encounter difficulties when confronted with unknown periods and the presence of multiple nonlinear seasonal components. To address these challenges, we propose a novel nonparametric approach based on periodic Gaussian process models, called sequential seasonal-trend decomposition (SSTD). This model is capable of extracting multiple seasonal components sequentially while estimating the component periods. A spectrum-regularized periodic Gaussian process is proposed to sequentially extract each of the seasonal components, leveraging Fourier basis functions to represent the remaining components. The unknown periods are estimated through a tailored two-step parameter estimation technique from the non-convex likelihood. To mitigate the computational complexity of the proposed method, we propose a circulant acceleration approach. By enabling the sequential extraction of multiple seasonal components and the estimation of unknown periods, SSTD bridges a gap in existing methodologies, yielding improved accuracy and efficiency. Empirical studies on synthetic and real-world data demonstrate its outperformance over current methods.
{"title":"Sequential Decomposition of Multiple Seasonal Components Using Spectrum-Regularized Periodic Gaussian Process","authors":"Yongxiang Li;Wuyang Zhang;Matthias Hwai Yong Tan;Peter Chien","doi":"10.1109/TSP.2025.3540720","DOIUrl":"10.1109/TSP.2025.3540720","url":null,"abstract":"Many real-world time series, such as electricity demand data, biomedical signals, and mechanical vibration signals, exhibit complex trends, encompass multiple seasonal (or periodic) components, and are prone to noise contamination. Existing decomposition methods encounter difficulties when confronted with unknown periods and the presence of multiple nonlinear seasonal components. To address these challenges, we propose a novel nonparametric approach based on periodic Gaussian process models, called sequential seasonal-trend decomposition (SSTD). This model is capable of extracting multiple seasonal components sequentially while estimating the component periods. A spectrum-regularized periodic Gaussian process is proposed to sequentially extract each of the seasonal components, leveraging Fourier basis functions to represent the remaining components. The unknown periods are estimated through a tailored two-step parameter estimation technique from the non-convex likelihood. To mitigate the computational complexity of the proposed method, we propose a circulant acceleration approach. By enabling the sequential extraction of multiple seasonal components and the estimation of unknown periods, SSTD bridges a gap in existing methodologies, yielding improved accuracy and efficiency. Empirical studies on synthetic and real-world data demonstrate its outperformance over current methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1034-1047"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1109/TSP.2025.3539883
Shuche Wang;Vincent Y. F. Tan
Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across several workers. However, scant attention has been paid to analyzing the behavior of distributed gradient descent algorithms in the presence of adversarial corruptions instead of random noise. In this paper, we formulate a novel problem in which adversarial corruptions are present in a distributed learning system. We show how to use ideas from (lazy) mirror descent to design a corruption-tolerant distributed optimization algorithm. Extensive convergence analysis for (strongly) convex loss functions is provided for different choices of the stepsize. We carefully optimize the stepsize schedule to accelerate the convergence of the algorithm, while at the same time amortizing the effect of the corruption over time. Experiments based on linear regression, support vector classification, and softmax classification on the MNIST dataset corroborate our theoretical findings.
{"title":"A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent","authors":"Shuche Wang;Vincent Y. F. Tan","doi":"10.1109/TSP.2025.3539883","DOIUrl":"10.1109/TSP.2025.3539883","url":null,"abstract":"Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across several workers. However, scant attention has been paid to analyzing the behavior of distributed gradient descent algorithms in the presence of adversarial corruptions instead of random noise. In this paper, we formulate a novel problem in which adversarial corruptions are present in a distributed learning system. We show how to use ideas from (lazy) mirror descent to design a corruption-tolerant distributed optimization algorithm. Extensive convergence analysis for (strongly) convex loss functions is provided for different choices of the stepsize. We carefully optimize the stepsize schedule to accelerate the convergence of the algorithm, while at the same time amortizing the effect of the corruption over time. Experiments based on linear regression, support vector classification, and softmax classification on the MNIST dataset corroborate our theoretical findings.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"827-842"},"PeriodicalIF":4.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143367350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1109/TSP.2025.3539605
Qinchen Wu;Jinping Sun;Bin Yang;Tao Shan;Yanping Wang
The standard multi-target transition density assumes that, conditional on the current multi-target state, targets survive and move independently of each other. Although this assumption is followed by most multi-target tracking (MTT) algorithms, it may not be applicable for tracking group targets exhibiting coordinated motion. This paper presents a principled Bayesian solution to tracking multiple resolvable group targets in the labeled random finite set framework. The transition densities of group targets with collective behavior are derived both for single-group and multi-group. For single-group, the transition density is characterized by a general labeled multi-target density and then approximated by the closest general labeled multi-Bernoulli (GLMB) density in terms of Kullback-Leibler divergence. For multi-group, we augment the group structure to multi-target states and propose a multiple group structure transition model (MGSTM) to recursively infer it. Additionally, the conjugation of the structure augmented multi-group multi-target density is also proved. An efficient implementation of multi-group multi-target tracker, named MGSTM-LMB filter, and its Gaussian mixture form are devised which preserves the first-order moment of multi-group multi-target density in recursive propagation. Numerical simulation results demonstrate the capability of the proposed MGSTM-LMB filter in multi-group scenes.
{"title":"Tracking Multiple Resolvable Group Targets With Coordinated Motion via Labeled Random Finite Sets","authors":"Qinchen Wu;Jinping Sun;Bin Yang;Tao Shan;Yanping Wang","doi":"10.1109/TSP.2025.3539605","DOIUrl":"10.1109/TSP.2025.3539605","url":null,"abstract":"The standard multi-target transition density assumes that, conditional on the current multi-target state, targets survive and move independently of each other. Although this assumption is followed by most multi-target tracking (MTT) algorithms, it may not be applicable for tracking group targets exhibiting coordinated motion. This paper presents a principled Bayesian solution to tracking multiple resolvable group targets in the labeled random finite set framework. The transition densities of group targets with collective behavior are derived both for single-group and multi-group. For single-group, the transition density is characterized by a general labeled multi-target density and then approximated by the closest general labeled multi-Bernoulli (GLMB) density in terms of Kullback-Leibler divergence. For multi-group, we augment the group structure to multi-target states and propose a multiple group structure transition model (MGSTM) to recursively infer it. Additionally, the conjugation of the structure augmented multi-group multi-target density is also proved. An efficient implementation of multi-group multi-target tracker, named MGSTM-LMB filter, and its Gaussian mixture form are devised which preserves the first-order moment of multi-group multi-target density in recursive propagation. Numerical simulation results demonstrate the capability of the proposed MGSTM-LMB filter in multi-group scenes.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1018-1033"},"PeriodicalIF":4.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143367351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper addresses the suboptimal energy efficiency of conventional digital precoding schemes in multiple-input multiple-output (MIMO) systems. Through an analysis of the power amplifier (PA) output power distribution associated with conventional precoders, it is observed that these power distributions can be quite uneven, resulting in large PA backoff (thus low efficiency) and high power consumption. To tackle this issue, we propose a novel approach called flat precoding, which aims to control the flatness of the power distribution within a desired interval. In addition to reducing PA power consumption, flat precoding offers the advantage of requiring smaller saturation levels for PAs, which reduces the size of PAs and lowers the cost. To incorporate the concept of flat power distribution into precoding design, we introduce a new lower-bound per-antenna power constraint alongside the conventional sum power constraint and the upper-bound per-antenna power constraint. By adjusting the lower-bound and upper-bound values, we can effectively control the level of flatness in the power distribution. We then seek to find a flat precoder that satisfies these three sets of constraints while maximizing the weighted sum rate (WSR). In particular, we develop efficient algorithms to design weighted minimum mean squared error (WMMSE) and zero-forcing (ZF)-type precoders with controllable flatness features that maximize WSR. Numerical results demonstrate that complete flat precoding approaches, where the power distribution is a straight line, achieve the best trade-off between spectral efficiency and energy efficiency for existing PA technologies. We also show that the proposed ZF and WMMSE precoding methods can approach the performance of their conventional counterparts with only the sum power constraint, while significantly reducing PA size and power consumption.
{"title":"Energy-Efficient Flat Precoding for MIMO Systems","authors":"Foad Sohrabi;Carl Nuzman;Jinfeng Du;Hong Yang;Harish Viswanathan","doi":"10.1109/TSP.2025.3537960","DOIUrl":"10.1109/TSP.2025.3537960","url":null,"abstract":"This paper addresses the suboptimal energy efficiency of conventional digital precoding schemes in multiple-input multiple-output (MIMO) systems. Through an analysis of the power amplifier (PA) output power distribution associated with conventional precoders, it is observed that these power distributions can be quite uneven, resulting in large PA backoff (thus low efficiency) and high power consumption. To tackle this issue, we propose a novel approach called flat precoding, which aims to control the flatness of the power distribution within a desired interval. In addition to reducing PA power consumption, flat precoding offers the advantage of requiring smaller saturation levels for PAs, which reduces the size of PAs and lowers the cost. To incorporate the concept of flat power distribution into precoding design, we introduce a new lower-bound per-antenna power constraint alongside the conventional sum power constraint and the upper-bound per-antenna power constraint. By adjusting the lower-bound and upper-bound values, we can effectively control the level of flatness in the power distribution. We then seek to find a flat precoder that satisfies these three sets of constraints while maximizing the weighted sum rate (WSR). In particular, we develop efficient algorithms to design weighted minimum mean squared error (WMMSE) and zero-forcing (ZF)-type precoders with controllable flatness features that maximize WSR. Numerical results demonstrate that complete flat precoding approaches, where the power distribution is a straight line, achieve the best trade-off between spectral efficiency and energy efficiency for existing PA technologies. We also show that the proposed ZF and WMMSE precoding methods can approach the performance of their conventional counterparts with only the sum power constraint, while significantly reducing PA size and power consumption.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"795-810"},"PeriodicalIF":4.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modern computationally-intensive applications often operate under time constraints, necessitating acceleration methods and distribution of computational workloads across multiple entities. However, the outcome is either achieved within the desired timeline or not, and in the latter case, valuable resources are wasted. In this paper, we introduce solutions for layered-resolution computation. These solutions allow lower-resolution results to be obtained at an earlier stage than the final result. This innovation notably enhances the deadline-based systems, as if a computational job is terminated due to time constraints, an approximate version of the final result can still be generated. Moreover, in certain operational regimes, a high-resolution result might be unnecessary, because the low-resolution result may already deviate significantly from the decision threshold, for example in AI-based decision-making systems. Therefore, operators can decide whether higher resolution is needed or not based on intermediate results, enabling computations with adaptive resolution. We present our framework for two critical and computationally demanding jobs: distributed matrix multiplication (linear) and model inference in machine learning (nonlinear). Our theoretical and empirical results demonstrate that the execution delay for the first resolution is significantly shorter than that for the final resolution, while maintaining overall complexity comparable to the conventional one-shot approach. Our experiments further illustrate how the layering feature increases the likelihood of meeting deadlines and enables adaptability and transparency in massive, large-scale computations.
{"title":"Successive Refinement in Large-Scale Computation: Expediting Model Inference Applications","authors":"Homa Esfahanizadeh;Alejandro Cohen;Shlomo Shamai;Muriel Médard","doi":"10.1109/TSP.2025.3537409","DOIUrl":"10.1109/TSP.2025.3537409","url":null,"abstract":"Modern computationally-intensive applications often operate under time constraints, necessitating acceleration methods and distribution of computational workloads across multiple entities. However, the outcome is either achieved within the desired timeline or not, and in the latter case, valuable resources are wasted. In this paper, we introduce solutions for layered-resolution computation. These solutions allow lower-resolution results to be obtained at an earlier stage than the final result. This innovation notably enhances the deadline-based systems, as if a computational job is terminated due to time constraints, an approximate version of the final result can still be generated. Moreover, in certain operational regimes, a high-resolution result might be unnecessary, because the low-resolution result may already deviate significantly from the decision threshold, for example in AI-based decision-making systems. Therefore, operators can decide whether higher resolution is needed or not based on intermediate results, enabling computations with adaptive resolution. We present our framework for two critical and computationally demanding jobs: distributed matrix multiplication (linear) and model inference in machine learning (nonlinear). Our theoretical and empirical results demonstrate that the execution delay for the first resolution is significantly shorter than that for the final resolution, while maintaining overall complexity comparable to the conventional one-shot approach. Our experiments further illustrate how the layering feature increases the likelihood of meeting deadlines and enables adaptability and transparency in massive, large-scale computations.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"811-826"},"PeriodicalIF":4.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143084157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-31DOI: 10.1109/TSP.2025.3537867
Javier Salazar Cavazos;Jeffrey A. Fessler;Laura Balzano
Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. However, some applications involve heterogeneous data that vary in quality due to noise characteristics associated with each data sample. Heteroscedastic methods aim to deal with such mixed data quality. This paper develops a subspace learning method, named ALPCAH, that can estimate the sample-wise noise variances and use this information to improve the estimate of the subspace basis associated with the low-rank structure of the data. Our method makes no distributional assumptions of the low-rank component and does not assume that the noise variances are known. Further, this method uses a soft rank constraint that does not require subspace dimension to be known. Additionally, this paper develops a matrix factorized version of ALPCAH, named LR-ALPCAH, that is much faster and more memory efficient at the cost of requiring subspace dimension to be known or estimated. Simulations and real data experiments show the effectiveness of accounting for data heteroscedasticity compared to existing algorithms. Code available at https://github.com/javiersc1/ALPCAH.
{"title":"ALPCAH: Subspace Learning for Sample-Wise Heteroscedastic Data","authors":"Javier Salazar Cavazos;Jeffrey A. Fessler;Laura Balzano","doi":"10.1109/TSP.2025.3537867","DOIUrl":"10.1109/TSP.2025.3537867","url":null,"abstract":"Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. However, some applications involve heterogeneous data that vary in quality due to noise characteristics associated with each data sample. Heteroscedastic methods aim to deal with such mixed data quality. This paper develops a subspace learning method, named ALPCAH, that can estimate the sample-wise noise variances and use this information to improve the estimate of the subspace basis associated with the low-rank structure of the data. Our method makes no distributional assumptions of the low-rank component and does not assume that the noise variances are known. Further, this method uses a soft rank constraint that does not require subspace dimension to be known. Additionally, this paper develops a matrix factorized version of ALPCAH, named LR-ALPCAH, that is much faster and more memory efficient at the cost of requiring subspace dimension to be known or estimated. Simulations and real data experiments show the effectiveness of accounting for data heteroscedasticity compared to existing algorithms. Code available at <uri>https://github.com/javiersc1/ALPCAH</uri>.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"876-886"},"PeriodicalIF":4.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-31DOI: 10.1109/TSP.2025.3536846
Xi Yao;Wei Dai
This paper presents a new approach to the recovery of spectrally sparse signals (SSS) from partially observed entries, addressing challenges posed by large-scale data and heavy-noise environments. The SSS reconstruction can be formulated as a non-convex low-rank Hankel recovery problem. Traditional formulations for SSS recovery often suffer from reconstruction inaccuracies due to unequally weighted norms and over-relaxation of the Hankel structure in noisy conditions. Additionally, a critical limitation of standard proximal gradient (PG) methods for solving this optimization problem is their slow convergence. We overcome this issue by introducing a more accurate formulation and proposing the Low-rank Projected Proximal Gradient (LPPG) method, designed to efficiently converge to stationary points through a two-step process. The first step involves a modified PG approach, allowing for a constant step size independent of the signal size, significantly accelerating the gradient descent phase. The second step employs a subspace projection strategy, optimizing within a low-rank matrix space to further decrease the objective function. Both steps of the LPPG method are meticulously tailored to exploit the intrinsic low-rank and Hankel structures of the problem, thereby enhancing computational efficiency. Numerical simulations demonstrate substantial improvements in both efficiency and recovery accuracy of the LPPG method compared to existing benchmark algorithms. This performance gain is particularly pronounced in scenarios with significant noise, showcasing the method's robustness and applicability to large-scale SSS recovery tasks.
{"title":"A Low-Rank Projected Proximal Gradient Method for Spectral Compressed Sensing","authors":"Xi Yao;Wei Dai","doi":"10.1109/TSP.2025.3536846","DOIUrl":"10.1109/TSP.2025.3536846","url":null,"abstract":"This paper presents a new approach to the recovery of spectrally sparse signals (SSS) from partially observed entries, addressing challenges posed by large-scale data and heavy-noise environments. The SSS reconstruction can be formulated as a non-convex low-rank Hankel recovery problem. Traditional formulations for SSS recovery often suffer from reconstruction inaccuracies due to unequally weighted norms and over-relaxation of the Hankel structure in noisy conditions. Additionally, a critical limitation of standard proximal gradient (PG) methods for solving this optimization problem is their slow convergence. We overcome this issue by introducing a more accurate formulation and proposing the Low-rank Projected Proximal Gradient (LPPG) method, designed to efficiently converge to stationary points through a two-step process. The first step involves a modified PG approach, allowing for a constant step size independent of the signal size, significantly accelerating the gradient descent phase. The second step employs a subspace projection strategy, optimizing within a low-rank matrix space to further decrease the objective function. Both steps of the LPPG method are meticulously tailored to exploit the intrinsic low-rank and Hankel structures of the problem, thereby enhancing computational efficiency. Numerical simulations demonstrate substantial improvements in both efficiency and recovery accuracy of the LPPG method compared to existing benchmark algorithms. This performance gain is particularly pronounced in scenarios with significant noise, showcasing the method's robustness and applicability to large-scale SSS recovery tasks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"691-705"},"PeriodicalIF":4.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-31DOI: 10.1109/TSP.2025.3535822
Mojtaba Nazari;Anders Rosendal Korshøj;Naveed ur Rehman
A novel approach for decomposing a nonstationary signal into amplitude- and frequency-modulated (AM-FM) oscillations and discontinuous (jump) components is proposed. Current nonstationary signal decomposition methods are designed to either obtain constituent AM-FM oscillatory modes or the discontinuous and residual components from the data, separately. Yet, many real-world signals of interest simultaneously exhibit both behaviors i.e., jumps and oscillations. Currently, no available method can extract jumps and AM-FM oscillatory components directly from the data. In our novel approach, we design and solve a variational optimization problem to accomplish this task. The optimization formulation includes a regularization term to minimize the bandwidth of all signal modes for effective oscillation modeling, and a prior for extracting the jump component. Our approach addresses the limitations of conventional AM-FM signal decomposition methods in extracting jumps and the limitations of existing jump extraction methods in decomposing multiscale oscillations. By employing an optimization framework that accounts for both multiscale oscillatory components and discontinuities, the proposed method shows superior performance compared to existing decomposition techniques. We demonstrate the effectiveness of our approach on synthetic, real-world, single-channel, and multivariate data, highlighting its utility in three specific applications: earth's electric field signals, electrocardiograms (ECG), and electroencephalograms (EEG).
{"title":"Jump Plus AM-FM Mode Decomposition","authors":"Mojtaba Nazari;Anders Rosendal Korshøj;Naveed ur Rehman","doi":"10.1109/TSP.2025.3535822","DOIUrl":"10.1109/TSP.2025.3535822","url":null,"abstract":"A novel approach for decomposing a nonstationary signal into amplitude- and frequency-modulated (AM-FM) oscillations and discontinuous (jump) components is proposed. Current nonstationary signal decomposition methods are designed to either obtain constituent AM-FM oscillatory modes or the discontinuous and residual components from the data, separately. Yet, many real-world signals of interest simultaneously exhibit both behaviors i.e., jumps and oscillations. Currently, no available method can extract jumps and AM-FM oscillatory components directly from the data. In our novel approach, we design and solve a variational optimization problem to accomplish this task. The optimization formulation includes a regularization term to minimize the bandwidth of all signal modes for effective oscillation modeling, and a prior for extracting the jump component. Our approach addresses the limitations of conventional AM-FM signal decomposition methods in extracting jumps and the limitations of existing jump extraction methods in decomposing multiscale oscillations. By employing an optimization framework that accounts for both multiscale oscillatory components and discontinuities, the proposed method shows superior performance compared to existing decomposition techniques. We demonstrate the effectiveness of our approach on synthetic, real-world, single-channel, and multivariate data, highlighting its utility in three specific applications: earth's electric field signals, electrocardiograms (ECG), and electroencephalograms (EEG).","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1081-1093"},"PeriodicalIF":4.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-29DOI: 10.1109/TSP.2025.3536012
Gian Marti;Flurin Arquint;Christoph Studer
Spatial filtering based on multiple-input multiple-output (MIMO) processing is a promising approach to jammer mitigation. Effective MIMO data detectors that mitigate smart jammers have recently been proposed, but they all assume perfect time synchronization between transmitter(s) and receiver. However, to the best of our knowledge, there are no methods for resilient time synchronization in the presence of smart jammers. To remedy this situation, we propose JASS, the first method that enables reliable time synchronization for the single-user MIMO uplink while mitigating smart jamming attacks. JASS detects a randomized synchronization sequence based on a novel optimization problem that fits a spatial filter to the time-windowed receive signal in order to mitigate the jammer. We underscore the efficacy of the proposed optimization problem by proving that it ensures successful time synchronization under certain intuitive conditions. We then derive an efficient algorithm for approximately solving our optimization problem. Finally, we use simulations to demonstrate the effectiveness of JASS against a wide range of different jammer types.
{"title":"Jammer-Resilient Time Synchronization in the MIMO Uplink","authors":"Gian Marti;Flurin Arquint;Christoph Studer","doi":"10.1109/TSP.2025.3536012","DOIUrl":"10.1109/TSP.2025.3536012","url":null,"abstract":"Spatial filtering based on multiple-input multiple-output (MIMO) processing is a promising approach to jammer mitigation. Effective MIMO data detectors that mitigate <italic>smart</i> jammers have recently been proposed, but they all assume perfect time synchronization between transmitter(s) and receiver. However, to the best of our knowledge, there are no methods for resilient time synchronization in the presence of smart jammers. To remedy this situation, we propose JASS, the first method that enables reliable time synchronization for the single-user MIMO uplink while mitigating smart jamming attacks. JASS detects a randomized synchronization sequence based on a novel optimization problem that fits a spatial filter to the time-windowed receive signal in order to mitigate the jammer. We underscore the efficacy of the proposed optimization problem by proving that it ensures successful time synchronization under certain intuitive conditions. We then derive an efficient algorithm for approximately solving our optimization problem. Finally, we use simulations to demonstrate the effectiveness of JASS against a wide range of different jammer types.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"706-720"},"PeriodicalIF":4.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143057110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-29DOI: 10.1109/TSP.2025.3535691
Hongxia Miao;Mugen Peng
Integrated sensing and communications (ISAC) has been expected to be a key technique in the sixth-generation cellular networks. With the increase of carrier frequency (to millimeter-wave or Terahertz spectrum) and antenna array size (to extremely large-scale antenna) in wireless communications, the near-field area is enlarged and cannot be ignored. Accordingly, the channel model and its estimation algorithms are changed, which bring new chances in ISAC. However, the impact of both Doppler and spatial wideband effects, caused by high mobility and multicarriers, on sensing performance using communication signals is not well studied. In this study, these two effects are shown to be helpful in user sensing. First, the channel model is proposed for a high-speed moving user transmitting an orthogonal frequency division multiplex (OFDM) signal, where there are six unknown parameters. Then, the Cramer-Rao lower bounds (CRLB) for joint six parameter estimation is determined, where the impact of the near-field parameter and the velocity on the CRLB of positioning are discussed and quantified. Further, to compensate for the deficiency that the CRLB is tight only in high signal-to-noise-ratio (SNR) scenarios, we derive the Ziv-Zakai bound (ZZB) for positioning by exploiting the prior information on positioning parameters. Subsequently, a joint position and velocity parameter estimation algorithm is designed by first performing a discrete fractional Fourier transform on the received signal to obtain a coarse estimation and then refining it by Newton-based refinement. Numerical results coincide with our analysis.
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