IEEE Transactions on Signal Processing最新文献_第9页

Quantization-Aware EE Optimization and SE-EE Tradeoff for MiLAC-Aided MU-MISO Beamforming milac辅助MU-MISO波束形成的量化感知EE优化和SE-EE权衡

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-09-24 DOI: 10.1109/TSP.2026.3736279

Yuchen Zhang;Pinjun Zheng;Tareq Y. Al-Naffouri

{"title":"Quantization-Aware EE Optimization and SE-EE Tradeoff for MiLAC-Aided MU-MISO Beamforming","authors":"Yuchen Zhang;Pinjun Zheng;Tareq Y. Al-Naffouri","doi":"10.1109/TSP.2026.3736279","DOIUrl":"https://doi.org/10.1109/TSP.2026.3736279","url":null,"abstract":"In large antenna arrays, hardware power consumption becomes a dominant design constraint, making energy efficiency (EE) a primary design objective alongside spectral efficiency (SE). Microwave linear analog computer (MiLAC)-aided beamforming, whose front end is a passive reciprocal stream-to-antenna network, emerges as a promising candidate to address this tradeoff by reducing the number of active radio-frequency chains to the stream number, at a moderate SE cost. Despite this promise, no EE optimization framework has been established for MiLAC-aided beamforming that accounts for digital-to-analog converter quantization noise and post-quantized transmit power. We fill this gap for downlink multiuser multiple-input single-output systems by formulating quantization-aware EE maximization over the MiLAC-feasible beamformer and characterizing the resulting SE-EE tradeoff. Three contributions follow. First, we prove a row-space optimality property of the effective MiLAC-aided beamformer, yielding an equivalent reduced-dimension reformulation whose complexity scales with the stream number rather than the antenna number. Second, we develop a low-complexity Dinkelbach-weighted minimum mean-square error algorithm aided by projected gradient descent that is guaranteed to converge to a stationary point. Third, we cast the SE-EE tradeoff as a multi-objective optimization problem and trace the tradeoff boundary via the weighted-sum method. An alternative reduced-dimension variable removes the bilinearity in the original parameterization, and an auxiliary-variable lift combined with successive convex approximation yields a convex per-iteration subproblem with guaranteed convergence. Numerical results on a DeepMIMO v4 deployment demonstrate that MiLAC-aided beamforming substantially improves EE over digital and hybrid benchmarks at a moderate SE cost. The tradeoff boundary analysis further shows that MiLAC-aided beamforming significantly expands the achievable SE-EE operating region relative to digital and hybrid architectures.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"4225-4240"},"PeriodicalIF":5.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131648","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

Radio Map Estimation via Latent Domain Plug-and-Play Denoising 基于隐域即插即用去噪的无线电地图估计

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-01-05 DOI: 10.1109/TSP.2025.3650699

Le Xu;Lei Cheng;Junting Chen;Wenqiang Pu;Xiao Fu

{"title":"Radio Map Estimation via Latent Domain Plug-and-Play Denoising","authors":"Le Xu;Lei Cheng;Junting Chen;Wenqiang Pu;Xiao Fu","doi":"10.1109/TSP.2025.3650699","DOIUrl":"10.1109/TSP.2025.3650699","url":null,"abstract":"Radio map estimation (RME), also known as <italic>spectrum cartography</i>, aims to reconstruct the strength of radio interference across different domains (e.g., space and frequency) from sparsely sampled measurements. To tackle this typical inverse problem, state-of-the-art RME methods rely on handcrafted or data-driven structural information of radio maps. However, the former often struggles to model complex radio frequency (RF) environments and the latter requires excessive training—making it hard to quickly adapt to <italic>in situ</i> sensing tasks. This work presents an RME approach based on <italic>plug-and-play</i> (PnP) denoising, a technique from computational imaging. The idea is to leverage the observation that the denoising operations of signals like natural images and radio maps are similar—despite the nontrivial differences of the signals themselves. Hence, sophisticated denoisers designed for or learned from natural images can be directly employed to assist RME, avoiding using radio map data for training. Unlike conventional PnP methods that operate directly in the data domain, the proposed method exploits the classical spatio-spectral decomposition model of radio maps and proposes an ADMM algorithm that denoises in the latent factor that only represents the spatial domain. This design significantly improves computational efficiency and enhances noise robustness. Theoretical aspects, e.g., recoverability of the complete radio map and convergence of the ADMM algorithm are analyzed. Synthetic and real data experiments are conducted to demonstrate the effectiveness of our approach.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"3240-3253"},"PeriodicalIF":5.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902811","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

Locally Distributed Estimation With Binary Sensors: A Dissipativity-Based Approach 二值传感器局部分布估计：一种基于耗散的方法

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-08-14 DOI: 10.1109/TSP.2026.3723888

Shufen Ding;Deyuan Meng;Kaiquan Cai;Juntao Li

{"title":"Locally Distributed Estimation With Binary Sensors: A Dissipativity-Based Approach","authors":"Shufen Ding;Deyuan Meng;Kaiquan Cai;Juntao Li","doi":"10.1109/TSP.2026.3723888","DOIUrl":"10.1109/TSP.2026.3723888","url":null,"abstract":"In this paper, a distributed estimation algorithm is proposed to address the <inline-formula><tex-math>$boldsymbol{H}_{{infty}}$</tex-math></inline-formula>-consensus state estimation challenges over binary sensor networks. The algorithm extracts richer information from limited binary data by modeling the time-varying threshold of binary sensors as a linear combination of switching measurements at two consecutive time instants. By incorporating the constraint induced by the switching events, a novel distributed estimator is designed, which integrates data from both the sensor itself and neighboring nodes within its sensing range to enhance estimation accuracy. In addition, a local performance analysis framework is developed based on the vector dissipativity theory such that the proposed distributed estimation algorithm can be independently executed at each node, significantly reducing the computational complexity. Furthermore, sufficient conditions for the desired <inline-formula><tex-math>$boldsymbol{H}_{{infty}}$</tex-math></inline-formula>-consensus performance are derived, and a systematic procedure for directly calculating the estimator gains is formulated as linear matrix inequalities. Both theoretical analysis and simulation results demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"3569-3581"},"PeriodicalIF":5.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728396","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

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

Interleaved Transceiver Design for a Continuous-Transmission MIMO-OFDM ISAC System 连续传输MIMO-OFDM ISAC系统的交错收发器设计

IF 5.5 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2026-01-01 Epub Date: 2026-08-11 DOI: 10.1109/TSP.2026.3722759

Yating Chen;Cai Wen;Yan Huang;Jinye Peng;Wei Hong;Timothy N. Davidson

{"title":"Interleaved Transceiver Design for a Continuous-Transmission MIMO-OFDM ISAC System","authors":"Yating Chen;Cai Wen;Yan Huang;Jinye Peng;Wei Hong;Timothy N. Davidson","doi":"10.1109/TSP.2026.3722759","DOIUrl":"10.1109/TSP.2026.3722759","url":null,"abstract":"This paper proposes an interleaved transmission architecture for a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system that enables continuous data transmission while facilitating receiver processing reminiscent of pulse-based radar. A key feature of this architecture is that we can jointly design each transmitted ISAC block and its sensing filter to mitigate the high sidelobes that arise from the inter-block interference caused by targets at different ranges. We provide an exemplar of such a design in which constructive interference is integrated into the communication aspects, and the integrated mainlobe-to-sidelobe ratio (IMSR) of the beampattern is used to ensure desirable directivity of the sensing. In addition, the power of each time sample is constrained to manage the peak-to-average power ratio (PAPR). The joint transmitter-sensor design problem is addressed using an alternating optimization (AO) framework, with the subproblem for transmitted waveform design being solved via the successive convex approximation (SCA) method. To further enhance computational efficiency, the alternating direction penalty method (ADPM) is employed to solve the subproblems within the SCA iterations. The convergence of ADPM is established, with convergence of the case of more than two auxiliary variables being established for the first time. Numerical simulations validate the effectiveness of our proposed approach in achieving desirable performance in both radar sensing and communication, with the fast algorithm achieving comparable performance with greater computational efficiency.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"3676-3691"},"PeriodicalIF":5.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728231","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

Optimistic Thompson Sampling for No-Regret Learning in Unknown Games 关于未知游戏无悔学习的乐观汤普森抽样分析

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.2026.3695892

Yingru Li;Liangqi Liu;Wenqiang Pu;Hao Liang;Zhi-Quan Luo

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