IEEE Transactions on Signal Processing最新文献_第10页

LLM-ABBA: Understanding Time Series via Symbolic Approximation LLM-ABBA：通过符号逼近理解时间序列

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-02-16 DOI: 10.1109/TSP.2026.3662011

Xinye Chen;Erin Carson;Cheng Kang

{"title":"LLM-ABBA: Understanding Time Series via Symbolic Approximation","authors":"Xinye Chen;Erin Carson;Cheng Kang","doi":"10.1109/TSP.2026.3662011","DOIUrl":"10.1109/TSP.2026.3662011","url":null,"abstract":"The success of large language models (LLMs) for time series has been demonstrated in previous work. Utilizing a symbolic time series representation, one can efficiently bridge the gap between LLMs and time series. However, the remaining challenge is to exploit the semantic information hidden in time series by using symbols or existing tokens of LLMs, while aligning the embedding space of LLMs according to the hidden information of time series. The symbolic time series approximation (STSA) method called adaptive Brownian bridge-based symbolic aggregation (ABBA) shows outstanding efficacy in preserving salient time series features by modeling time series patterns in terms of amplitude and period while using existing tokens of LLMs. In this paper, we introduce a method, called LLM-ABBA, that integrates ABBA into large language models for various downstream time series tasks. By symbolizing time series, LLM-ABBA compares favorably to the recent state-of-the-art (SOTA) in UCR and three medical time series classification tasks. Meanwhile, a fixed-polygonal chain trick in ABBA is introduced to avoid obvious drifting during forecasting tasks by significantly mitigating the effects of cumulative error arising from misused symbols during the transition from symbols to numerical values. In time series regression tasks, LLM-ABBA achieves the new SOTA on Time Series Extrinsic Regression (TSER) benchmarks. LLM-ABBA also shows competitive forecasting capability compared to recent SOTA time series forecasting results. We believe this framework can also seamlessly extend to other time series tasks. Our simulation code is publicly available at: <uri>https://github.com/inEXASCALE/llm-abba</uri>.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"1218-1232"},"PeriodicalIF":5.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146205429","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 Blind Recovery Algorithm for Multicoset Sampling via Graph Spectral Support Identification 基于图谱支持识别的多集采样盲恢复算法

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-08-19 DOI: 10.1109/TSP.2026.3725870

Deyun Wei;Fangdelong Du

{"title":"A Blind Recovery Algorithm for Multicoset Sampling via Graph Spectral Support Identification","authors":"Deyun Wei;Fangdelong Du","doi":"10.1109/TSP.2026.3725870","DOIUrl":"10.1109/TSP.2026.3725870","url":null,"abstract":"A predominant limitation of current graph sampling approaches is their inability to reliably process multiband graph signals with unknown spectral support. In this paper, we present a minimal graph sampling rate for multiband graph signals, which standardizes the minimum number of required samples for both scenarios: known graph spectral support and unknown graph spectral support. Moreover, we propose a multicoset graph sampling method to achieve sub-Nyquist sampling rates. This multicoset graph sampling is essentially a type of periodic non-uniform sampling method. Furthermore, based on the graph continuous-to-finite block (GCFB), we propose two multicoset blind recovery (MCBR) algorithms. One is entitled the MCBR1 algorithm and uses a single instance of the GCFB. When the sampling rate is sufficiently high, the MCBR1 algorithm guarantees favorable reconstruction performance. The other is referred to as the MCBR2 algorithm and involves a bi-section procedure and several uses of the GCFB. The MCBR2 algorithm operates at the minimum sampling rate and can accurately identify the graph spectral support. Finally, by comparing the mean squared error (MSE), signal-to-noise ratio (SNR) and success rate of different methods, the experiments show that our MCBR2 algorithm has the smallest reconstruction error and best noise resistance. Practical validation on the real-world Intel Berkeley wireless sensor network dataset further verifies the practicability and scalability of the proposed sampling and recovery framework.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"3692-3707"},"PeriodicalIF":5.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148767879","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

An Optimization Algorithm for State Estimation Under Heavy-Tailed and Skewed Noise 一种重尾偏态噪声下的状态估计优化算法

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-08-17 DOI: 10.1109/TSP.2026.3724240

Yifan Yu;Daniel P. Palomar

引用次数: 0

Novel Receiver Designs for Low-Resolution ADC-Based Uplink MU-MIMO Systems With Unknown Channel Covariance Information 信道协方差信息未知的低分辨率adc上行MU-MIMO系统接收机设计

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-08-06 DOI: 10.1109/TSP.2026.3721208

Rubin Jose Peter;Chandra R. Murthy

{"title":"Novel Receiver Designs for Low-Resolution ADC-Based Uplink MU-MIMO Systems With Unknown Channel Covariance Information","authors":"Rubin Jose Peter;Chandra R. Murthy","doi":"10.1109/TSP.2026.3721208","DOIUrl":"https://doi.org/10.1109/TSP.2026.3721208","url":null,"abstract":"It is known that joint channel estimation and soft symbol decoding enhance the performance of multiple-input multiple-output (MIMO) communication systems with low-resolution analog-to-digital converters (ADCs). However, existing techniques require accurate knowledge of channel statistics to be effective. In this paper, we consider an uplink multi-user MIMO system and develop novel algorithms for joint estimation of channel coefficients and statistics along with soft symbol decoding. Specifically, we develop a sequential processing technique, based on variational Bayesian inference, for jointly estimating the covariance matrices of all users’ channels, the corresponding channel matrices, and soft symbols. We also develop an alternative block processing scheme for the same problem. Corresponding algorithms for sequential and block processing of unquantized data can be obtained as simplifications of our algorithms. On the analytical side, we derive a marginalized Bayesian Cramér-Rao Lower Bound (MB-CRLB) for covariance matrix estimation and elucidate the effect of the number of snapshots and SNR on the estimation error. Finally, we empirically evaluate the new algorithms and demonstrate their superior performance in scenarios where the channel statistics are unknown. The results also show that the mean squared error in channel covariance matrix estimation via the proposed algorithm closely follows the behavior of the MB-CRLB as a function of the SNR and number of frames.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"3724-3739"},"PeriodicalIF":5.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925065","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

Robust Transceiver Design for Wideband MIMO Radar Under Space-Frequency Scheme 空频下宽带MIMO雷达的鲁棒收发器设计

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-09-09 DOI: 10.1109/TSP.2026.3732566

Junhui Qian;Jiheng Liu;Yujie Zou;Xiaobing Song;Jing Yang;Guobing Qian

{"title":"Robust Transceiver Design for Wideband MIMO Radar Under Space-Frequency Scheme","authors":"Junhui Qian;Jiheng Liu;Yujie Zou;Xiaobing Song;Jing Yang;Guobing Qian","doi":"10.1109/TSP.2026.3732566","DOIUrl":"10.1109/TSP.2026.3732566","url":null,"abstract":"Aiming at the problem of angle uncertainty in wideband Multiple-Input Multiple-Output (MIMO) radar system, this paper investigates the robust design of space-frequency joint design for radar transceiver. Firstly, a system model considering angle disturbance is established, and the average Signal-to-Interference-Noise Ratio (SINR) in statistical sense is introduced as a performance indicator to enhance the robustness of target detection. Subsequently, two types of joint optimization strategies are developed: one is a joint optimization method with SINR and Integrated Mainlobe to Sidelobe Ratio (IMSR) as weighted objectives; the other is the SINR maximization problem within a <inline-formula><tex-math>$boldsymbol{varepsilon}$</tex-math></inline-formula> constraint framework. On this basis, combining the Maximization-Minimization (MM) framework, the Dinkelbach framework with the adaptive Alternating Direction Penalty Method (ADPM), the corresponding multi-objective iterative optimization procedures are proposed to comprehensively optimize the mainlobe energy concentration and sidelobe leakage suppression performance, thereby improving the robustness of the algorithm and the flexibility of parameter adjustment. The simulation results verify the superiority of the proposed method in convergence speed and detection performance in multi-target, strong interference and angle uncertainty scenarios.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"4356-4371"},"PeriodicalIF":5.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148915531","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

Dynamic Decomposition Diffusion Model for Probabilistic Multivariate Time Series Forecasting 概率多元时间序列预测的动态分解扩散模型

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-09-11 DOI: 10.1109/TSP.2026.3733192

Yuxin Li;Ao Wang;Xinyue Hu;Bo Chen;Wenchao Chen;Penghui Wang;Hongwei Liu;Mingyuan Zhou

引用次数: 0

Learned Universal Entropy Coding With Tensor Networks 用张量网络学习通用熵编码

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-09-11 DOI: 10.1109/TSP.2026.3733034

Xiaoxuan Fan;Wenrui Dai;Shaohui Li;Wen Fei;Chenglin Li;Junni Zou;Hongkai Xiong

{"title":"Learned Universal Entropy Coding With Tensor Networks","authors":"Xiaoxuan Fan;Wenrui Dai;Shaohui Li;Wen Fei;Chenglin Li;Junni Zou;Hongkai Xiong","doi":"10.1109/TSP.2026.3733034","DOIUrl":"https://doi.org/10.1109/TSP.2026.3733034","url":null,"abstract":"Existing methods for universal entropy coding suffer from the dilemma between versatility to general data sources and ability in exploiting complex structured statistics. They usually rely on presumed structural and statistical assumptions to decompose joint probability distribution for tractable entropy modeling. To address this problem, in this paper, we propose the first learned universal entropy coding method that leverages tensor networks (TNs) to learn joint probability distribution of diverse data sources without presuming distribution priors or context structures. Specifically, we resort to the tensor network formulation based on matrix product states (MPS) to learn the tractable low-order tensor approximation for high-order tensor representation of joint probability distribution. Entropy encoding and decoding are achieved in a sequential fashion by constructing cumulative distribution functions with marginal probabilities of sources on top of the probabilistic modeling. We demonstrate in theory that the proposed entropy model converges to stationary distribution, guarantees upper and lower bounds of entropy rates, and exponentially decays in truncation error of entropy rate with truncation order and correlation length. Furthermore, TN-based practical coding framework is designed with unified Gray-code bit-plane decomposition to mitigate numerical overflow. Experimental results demonstrate that the proposed method consistently outperforms existing universal approaches and is comparable to task-specific approaches in the tasks of lossless graph and image compression and learned lossy image compression.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"4402-4418"},"PeriodicalIF":5.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130960","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

Constellation Selection and Power Control for OFDM-Based ISAC: From Theory to Prototype 基于ofdm的ISAC星座选择与功率控制：从理论到原型

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-03-25 DOI: 10.1109/TSP.2026.3696580

Kaitao Meng;Kawon Han;Christos Masouros;Fan Liu

{"title":"Constellation Selection and Power Control for OFDM-Based ISAC: From Theory to Prototype","authors":"Kaitao Meng;Kawon Han;Christos Masouros;Fan Liu","doi":"10.1109/TSP.2026.3696580","DOIUrl":"https://doi.org/10.1109/TSP.2026.3696580","url":null,"abstract":"Integrated sensing and communication (ISAC) techniques can leverage existing, wide-coverage communication networks to perform sensing tasks, enabling large-scale and low-cost target sensing. However, the inherent randomness of communication data payloads introduces undesired sidelobes in the ambiguity function that may degrade target detection and parameter estimation performance. This paper develops a communication-centric ISAC framework that is standards-compliant and compatible with existing devices. Specifically, we propose a low-complexity constellation selection scheme over a <italic>finite</i>, <italic>off-the-shelf</i> alphabet, achieving an efficient sensing-communication trade-off without custom waveforms or frame-structure changes. To this end, we analyze two classical sensing receivers including matched filtering (MF) and reciprocal filtering (RF) for ranging measurements, and derive closed-form sensing laws that link constellation statistics to sensing performance. Under any finite-alphabet constellation combination, MF sidelobes depend on the weighted sum of the kurtosis values of the per-subcarrier constellations, while RF noise enhancement depends on the inverse second moment of the transmit symbol, providing a tractable expression for tuning the sensing-communication trade-off. Guided by these insights, we propose a low-complexity design that retains a finite constellation set and shapes power under communication quality constraints. We prove that in flat-fading channels, there exists a Pareto-optimal solution that activates no more than three constellations. For frequency-selective channels, a bilevel algorithm with closed-form inner updates attains near-optimal performance while sharply reducing computational complexity. We validate the entire theoretical pipeline with numerical simulations as well as experimental results. The results corroborate the theory, constellation mixing and coherent multi-symbol processing reliably reveal markedly weaker targets at practical signal-to-noise ratios.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"4131-4147"},"PeriodicalIF":5.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131584","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 Block-Term Tensor Decomposition and Its Application to Image Processing 协同块项张量分解及其在图像处理中的应用

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-03-22 DOI: 10.1109/TSP.2026.3695814

Hao Zhang;Xi-Le Zhao;Junjun Pan;Kai-Wen Guo;Jiashu Zhang

{"title":"Collaborative Block-Term Tensor Decomposition and Its Application to Image Processing","authors":"Hao Zhang;Xi-Le Zhao;Junjun Pan;Kai-Wen Guo;Jiashu Zhang","doi":"10.1109/TSP.2026.3695814","DOIUrl":"https://doi.org/10.1109/TSP.2026.3695814","url":null,"abstract":"The rank-<inline-formula><tex-math>$(L,M,N)$</tex-math></inline-formula> block-term tensor decomposition (termed as BTD for convenience), which decomposes a tensor into a summation of multiple block-terms, has shown promise in multidimensional signal processing. However, in real-world applications, different components of a tensor usually have common and individual structures (e.g., shared dictionaries and individual coefficients), while BTD neglects the common structures across different components. Moreover, the corresponding algorithms for BTD (e.g., alternating least squares algorithm (BTD-ALS)) are computationally expensive for large-scale real data. To address these problems, we propose a collaborative block-term tensor decomposition (termed as CBTD), which decomposes a tensor into a summation of different block-terms multiplied by shared matrices. Compared with BTD, CBTD can better capture the structure of the data (i.e., the shared dictionaries and individual coefficients of multiple components), which is important especially in the challenging noisy/missing cases; and CBTD essentially projects the original tensor into a low-dimensional latent tensor by the shared factor matrices and then performs BTD on the latent tensor, which allows us to design more efficient algorithms. Moreover, we discuss the connection between BTD and CBTD and establish the essential uniqueness for CBTD. Empowered with CBTD, we propose the multidimensional signal reconstruction model and develop the corresponding proximal alternating minimization-based algorithm (CBTD-PAM) with the convergence guarantee. Extensive experiments on multispectral images and gray videos under Gaussian/sparse noise removal and tensor completion tasks demonstrate that the CBTD-PAM improves the reconstruction performance over competing methods in most cases. Especially, compared to the BTD-ALS, CBTD-PAM not only delivers higher quality results but also achieves substantial speedup.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"2724-2739"},"PeriodicalIF":5.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443232","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

Nonlinear System Identification Through Amplitude-Phase-Frequency Characterization 基于幅相频特性的非线性系统辨识

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-06-10 DOI: 10.1109/TSP.2026.3702569

Xiaojing Huang

{"title":"Nonlinear System Identification Through Amplitude-Phase-Frequency Characterization","authors":"Xiaojing Huang","doi":"10.1109/TSP.2026.3702569","DOIUrl":"https://doi.org/10.1109/TSP.2026.3702569","url":null,"abstract":"Based on expanded memory polynomial (EMP) and orthogonal EMP (OEMP), this paper addresses the nonlinear system identification (NSI) problem by introducing an amplitude-phase-frequency (APF) characterization framework to enable performance measurability, monotonicity, and optimality. APF characteristic functions (CFs) are defined to better capture the nonlinear distortions arising from both the amplitude and phase of the input signal. It is shown that an APF CF can be expressed as a weighted sum of characteristic basis functions (CBFs) or their orthogonal counterparts (OCBFs), with the associated CBF or OCBF frequency responses directly linked to the EMP or OEMP coefficients. As a result, NSI can be carried out by alternatively estimating the CBF or OCBF frequency responses. The paper also presents detailed estimation methods employing frequency modulated continuous wave (FMCW) test signals and analyzes different mean-squared-error (MSE) measurement strategies. Simulation and experimental results using a 73.5 GHz carrier frequency and 2.125 GHz bandwidth millimeter wave communication system validate the effectiveness and practical applicability of the proposed technique.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"2534-2549"},"PeriodicalIF":5.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148443303","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