IEEE Transactions on Signal Processing最新文献_第5页

A Method to Obtain Non-Power-of-Two FFT Flow Graphs Based on a New Prime Factor Algorithm

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-12 DOI: 10.1109/TSP.2025.3540561

VÍctor Manuel Bautista;Mario Garrido

{"title":"A Method to Obtain Non-Power-of-Two FFT Flow Graphs Based on a New Prime Factor Algorithm","authors":"VÍctor Manuel Bautista;Mario Garrido","doi":"10.1109/TSP.2025.3540561","DOIUrl":"10.1109/TSP.2025.3540561","url":null,"abstract":"This paper presents a novel method to obtain non-power-of-two (NP2) fast Fourier transform (FFT) flow graphs based on a new prime factor algorithm (PFA). The FFT flow graph is crucial for designing FFT architectures but previous works only provide systematic approaches to build flow graphs for power-of-two sizes (P2). Thus, the derivation of NP2 flow graphs is an important step towards the design of efficient NP2 FFT architectures. The proposed approach consists of two independent parts. On the one hand, it obtains all the possible index mappings that lead to a flow graph with no rotations between butterflies. On the other hand, it determines the permutations between butterflies in the flow graph. By combining these two parts, the order of the inputs and outputs is derived. As a result, the entire flow graph is obtained systematically. Additionally, the proposed approach generates all the possible flow graphs for a given factorization of the FFT size. The reduction in operations for NP2 FFTs using the proposed approach leads to a significant reduction in area and power consumption concerning P2 FFTs with similar sizes after implementing the proposed flow graphs directly in hardware. Particularly, there is a significant improvement between the proposed 30-point and 60-point FFT and previous efficient P2 FFTs. This remarkable fact sets NP2 at the forefront of FFT research after being in second place behind P2 FFTs for decades.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1004-1017"},"PeriodicalIF":4.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10883046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAML-KalmanNet: A Neural Network-Assisted Kalman Filter Based on Model-Agnostic Meta-Learning

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-12 DOI: 10.1109/TSP.2025.3540018

Shanli Chen;Yunfei Zheng;Dongyuan Lin;Peng Cai;Yingying Xiao;Shiyuan Wang

{"title":"MAML-KalmanNet: A Neural Network-Assisted Kalman Filter Based on Model-Agnostic Meta-Learning","authors":"Shanli Chen;Yunfei Zheng;Dongyuan Lin;Peng Cai;Yingying Xiao;Shiyuan Wang","doi":"10.1109/TSP.2025.3540018","DOIUrl":"10.1109/TSP.2025.3540018","url":null,"abstract":"Neural network-assisted (NNA) Kalman filters provide an effective solution to addressing the filtering issues involving partially unknown system information by incorporating neural networks to compute the intermediate values influenced by unknown data, such as the Kalman gain in the filtering process. However, whenever there are slight changes in the state-space model (SSM), previously trained networks used in NNA Kalman filters become outdated, necessitating extensive time and data for retraining. Furthermore, obtaining sufficient labeled data for supervised learning is costly, and the effectiveness of unsupervised learning can be inconsistent. To this end, to address the inflexibility of neural network architecture and the scarcity of training data, we propose a model-agnostic meta-learning based neural network-assisted Kalman filter in this paper, called MAML-KalmanNet, by employing a limited amount of labeled data and training rounds to achieve desirable outcomes comparable to the supervised NNA Kalman filters with sufficient training. MAML-KalmanNet utilizes a pre-training approach based on specifically tailored meta-learning, enabling the network to adapt to model changes with minimal data and time without the requirement of retraining. Simultaneously, by fully leveraging the information from the SSM, MAML-KalmanNet eliminates the requirement of a large amount of labeled data to train the meta-learning initialization network. Simulations show that MAML-KalmanNet can mitigate the shortcomings existing in NNA Kalman filters regarding the requirements of abundant training data and sensitive network architecture, while providing real-time state estimation across a range of noise distributions.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"988-1003"},"PeriodicalIF":4.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401957","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}

引用次数: 0

Communication-Efficient Vertical Federated Learning via Compressed Error Feedback

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-11 DOI: 10.1109/TSP.2025.3540655

Pedro Valdeira;João Xavier;Cláudia Soares;Yuejie Chi

{"title":"Communication-Efficient Vertical Federated Learning via Compressed Error Feedback","authors":"Pedro Valdeira;João Xavier;Cláudia Soares;Yuejie Chi","doi":"10.1109/TSP.2025.3540655","DOIUrl":"10.1109/TSP.2025.3540655","url":null,"abstract":"Communication overhead is a known bottleneck in federated learning (FL). To address this, lossy compression is commonly used on the information communicated between the server and clients during training. In horizontal FL, where each client holds a subset of the samples, such communication-compressed training methods have recently seen significant progress. However, in their vertical FL counterparts, where each client holds a subset of the features, our understanding remains limited. To address this, we propose an error feedback compressed vertical federated learning (<monospace>EF-VFL</monospace>) method to train split neural networks. In contrast to previous communication-compressed methods for vertical FL, <monospace>EF-VFL</monospace> does not require a vanishing compression error for the gradient norm to converge to zero for smooth nonconvex problems. By leveraging error feedback, our method can achieve a <inline-formula><tex-math>$mathcal{O}({1}/{T})$</tex-math></inline-formula> convergence rate for a sufficiently large batch size, improving over the state-of-the-art <inline-formula><tex-math>$mathcal{O}({1}/{sqrt{T}})$</tex-math></inline-formula> rate under <inline-formula><tex-math>$mathcal{O}({1}/{sqrt{T}})$</tex-math></inline-formula> compression error, and matching the rate of uncompressed methods. Further, when the objective function satisfies the Polyak-Łojasiewicz inequality, our method converges linearly. In addition to improving convergence, our method also supports the use of private labels. Numerical experiments show that <monospace>EF-VFL</monospace> significantly improves over the prior art, confirming our theoretical results.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1065-1080"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392952","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}

引用次数: 0

Collaborative Trajectory Optimization for Multitarget Tracking in Airborne Radar Network With Missing Data

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-11 DOI: 10.1109/TSP.2025.3540798

Juan Hu;Lei Zuo;Pramod K. Varshney;Zhengyu Lan;Yongchan Gao

{"title":"Collaborative Trajectory Optimization for Multitarget Tracking in Airborne Radar Network With Missing Data","authors":"Juan Hu;Lei Zuo;Pramod K. Varshney;Zhengyu Lan;Yongchan Gao","doi":"10.1109/TSP.2025.3540798","DOIUrl":"10.1109/TSP.2025.3540798","url":null,"abstract":"In this paper, an effective collaborative trajectory optimization (CTO) strategy is proposed for multitarget tracking in airborne radar networks with missing data. Missing data may occur during data exchange between radar nodes and a fusion center (FC) due to unreliability of communication channels. The CTO strategy aims to enhance the overall multi-target tracking performance by collaboratively optimizing the trajectories of airborne radars and the FC. In this paper, we derive the posterior Cramér-Rao lower bound (PCRLB) with missing data to evaluate the target tracking performance. On this basis, to maximize the target tracking performance while considering dynamics, collision avoidance, and communication distance constraints, we formulate the CTO optimization problem. The formulated problem is non-convex and internally coupled, which is challenging to solve directly. We decompose the CTO problem into two subproblems and devise an alternating optimization method. Specifically, approximation, and successive convex approximation are applied to make the subproblems solvable. Then, the two subproblems are solved alternately to realize the collaborative trajectory optimization of radars and the FC. Simulation results demonstrate that the proposed CTO strategy achieves better target tracking performance as compared with other benchmark strategies.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1048-1064"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392950","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}

引用次数: 0

Sequential Decomposition of Multiple Seasonal Components Using Spectrum-Regularized Periodic Gaussian Process

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-11 DOI: 10.1109/TSP.2025.3540720

Yongxiang Li;Wuyang Zhang;Matthias Hwai Yong Tan;Peter Chien

{"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}

引用次数: 0

A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-07 DOI: 10.1109/TSP.2025.3539883

Shuche Wang;Vincent Y. F. Tan

引用次数: 0

Tracking Multiple Resolvable Group Targets With Coordinated Motion via Labeled Random Finite Sets

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-07 DOI: 10.1109/TSP.2025.3539605

Qinchen Wu;Jinping Sun;Bin Yang;Tao Shan;Yanping Wang

{"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}

引用次数: 0

Energy-Efficient Flat Precoding for MIMO Systems 多输入多输出（MIMO）系统的高能效扁平精确编码

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-04 DOI: 10.1109/TSP.2025.3537960

Foad Sohrabi;Carl Nuzman;Jinfeng Du;Hong Yang;Harish Viswanathan

{"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}

引用次数: 0

Successive Refinement in Large-Scale Computation: Expediting Model Inference Applications

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-03 DOI: 10.1109/TSP.2025.3537409

Homa Esfahanizadeh;Alejandro Cohen;Shlomo Shamai;Muriel Médard

{"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}

引用次数: 0

ALPCAH: Subspace Learning for Sample-Wise Heteroscedastic Data

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-31 DOI: 10.1109/TSP.2025.3537867

Javier Salazar Cavazos;Jeffrey A. Fessler;Laura Balzano

{"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}

引用次数: 0