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Channel Estimation and Beamforming Design for MF-RIS-Aided Communication Systems

IEEE signal processing LETTERS 2025年 32卷 916-920页

作者： Pan, Zaihao Wang, Wen Nie, Gaofeng Zheng, Ailing Ni, Wanli Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Pervas Commun Ctr Purple Mt Labs Nanjing 211111 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

In this letter, we study the beamforming design for channel estimation of multi-functional reconfigurable intelligent surface (MF-RIS)-aided multi-user communications that supports simultaneous signal reflection, refraction, and amplification. A least square (LS) based channel estimator is proposed for MF-RIS by considering both the coupled MF-RIS beams and the introduced thermal noise. With the discrete fourier transform (DFT)-matrix, the MF-RIS beamforming design problem is simplified under the proposed LS channel estimator. The optimal MF-RIS beamforming design that achieves the Cram & eacute;r-Rao lower bound (CRLB) of channel estimator is obtained with the proposed alternating optimization algorithm. Simulation results demonstrate the effectiveness of the proposed beamforming design in reducing the impact of thermal noise.

关键词： Channel estimation Array signal processing Reconfigurable intelligent surfaces Noise Thermal noise signal processing algorithms Reflection Estimation error Optimization Lower bound Beamforming design channel estimation least square estimator multi-functional RIS

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Edge-Preserving Consensus by Exploring the Feedback-Based Masking Method

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IEEE signal processing LETTERS 2025年 32卷 936-940页

作者： He, Wei Jiang, Jinyang Liu, Tianyue Aikoun, Yves Fidele Mao, Yi Fudan Univ Acad Engn & Technol Shanghai Engn Res Ctr AI & Robot Shanghai 200433 Peoples R China Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Nanjing 210023 Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Hohai Univ Key Lab Maritime Intelligent Network Informat Tec Minist Educ Changzhou 213200 Peoples R China

This brief deals with the edge-preserving consensus in networked agent groups, which seeks to keep the information of the state trajectories of agents private by utilizing an edge-based decentralized protocol design. A novel type of decentralized feedback-based masking approach is adopted for designing local channel filters such that the agents reach consensus under the heterogenous channel filters, where the information of the filter associated with a specific communication link can only be known by the agents who share this communication link. Our design guarantees asymptotical convergence to the consensus value for both the cases of the non-recursive and recursive nominal filters. Moreover, the algorithms for designing the masking elements are provided, by using the parallel realization of several stable fundamental elements and the bisection method for searching the gain.

关键词： Privacy Protocols Transfer functions Eigenvalues and eigenfunctions signal processing algorithms Multi-agent systems Design methodology Artificial intelligence Wireless sensor networks Vehicle dynamics Edge-preserving consensus security distributed networks channel filter

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High-Accuracy DOA Estimation for Non-Collinear Sparse Uniform Array

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IEEE signal processing LETTERS 2025年 32卷 206-210页

作者： Wang, Hongyong Chen, Xiaolong Deng, Weibo Zhang, Caisheng Xue, Yonghua Naval Aviat Univ Yantai 264001 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China

Conventional sparse uniform arrays (SUAs) is composed of multiple identical and rigorously collinear uniform linear arrays. By adjusting the baseline length between the subarrays, the array aperture can be arbitrarily large, thus substantially improving the accuracy of the direction-of-arrival (DOA) estimation. However, in practical applications, it is challenging to meet the strict collinearity requirement due to geographical constraints. In this letter, to address this problem, we propose the non-collinear sparse uniform array (NCSUA) model to mitigate the influence of the non-ideal terrain and enhance the practicality of the SUA. A novel estimation algorithm is then proposed to resolve the angle ambiguity in NCSUA and effectively achieve high-accuracy DOA estimation. Compared with the conventional SUA, numerical simulation results demonstrate the superiority of NCSUA employing the new de-ambiguity algorithm in DOA estimation performance and practical applications.

关键词： Estimation signal processing algorithms Direction-of-arrival estimation Array signal processing Antenna arrays Accuracy Vectors Receiving antennas Radar Sparse matrices High-accuracy DOA estimation non-collinear array sparse uniform array

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DPO: Discrete Prompt Optimization for Vision-Language Models

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IEEE signal processing LETTERS 2025年 32卷 671-675页

作者： Liang, Nanhao Liu, Yong Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

In recent years, the emergence of large vision-language models (VLMs) has catalyzed the development of prompt learning, where networks are trained to enhance VLM performance by learning continuous prompts. However, traditional continuous prompt learning often struggles with challenges like overfitting to Base classes and a lack of interpretability due to the nature of prompt parameterization. To overcome these limitations, we introduce Discrete Prompt Optimization (DPO), a method that optimizes text prompts in discrete word-space. During training, scores are assigned to token embeddings, which are then used to select the most effective token sequence for the downstream task. DPO was tested across 11 diverse datasets, consistently outperforming baseline methods like CLIP and CoOp on Novel classes in most cases. This discrete approach not only reduces overfitting but also enhances transparency and model interpretability, enabling the learning of dataset-specific text prompts that are easily understandable.

关键词： Training Optimization Adaptation models Visualization Overfitting Vectors Vocabulary signal processing algorithms Stochastic processes Standards Prompt learning vision-language model

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Optimal Sample Acquisition for Optimally Weighted PCA From Heterogeneous Quality Sources

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IEEE signal processing LETTERS 2025年 32卷 1425-1429页

作者： Hong, David Balzano, Laura Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

Modern high-dimensional datasets are often formed by acquiring samples from multiple sources having heterogeneous quality, i.e., some sources are noisier than others. Collecting data in this manner raises the following natural question: what is the best way to collect the data (i.e., how many samples should be acquired from each source) given constraints (e.g., on time or energy)? In general, the answer depends on what analysis is to be performed. In this paper, we study the foundational signal processing task of estimating underlying low-dimensional principal components. Since the resulting dataset will be high-dimensional and will have heteroscedastic noise, we focus on the recently proposed optimally weighted PCA, which is designed specifically for this setting. We develop an efficient method for designing sample acquisitions that optimize the asymptotic performance of optimally weighted PCA given resource constraints, and we illustrate the proposed method through various case studies.

关键词： Principal component analysis Noise signal processing algorithms Costs Soft sensors Software algorithms Optimization Data models Training Image sensors Heterogeneous quality large-dimensional data principal component analysis sample acquisition design

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Range-Ambiguous Clutter Separation via Reweighted Atomic Norm Minimization With EPC-MIMO Radar

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IEEE signal processing LETTERS 2025年 32卷 146-150页

作者： Gao, Jie Zhu, Shengqi Lan, Lan Sui, Jinxin Li, Ximin Xidian Univ Sch Elect Engn Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

The existence of range ambiguity and range dependence will seriously deteriorate the performance of space-time adaptive processing (STAP). In this regard, an adaptive range-ambiguous clutter separation method suitable for the element-pulse coding (EPC)-multiple-input multiple-output (MIMO) radar is developed in this letter. By introducing the EPC factor in both transmit elements and pulses, the clutter located in different range-ambiguous regions can be distinguished in the transmit spatial frequency dimension. Particularly, to ensure the separated performance of range-ambiguous clutter, the EPC factor is designed. Moreover, an approach on the basis of reweighted atomic norm minimization (RANM) is developed to separate the range-ambiguous clutter, leveraging the transmit spatial frequencies of clutter located in various range ambiguity areas. Furthermore, after clutter separation, the clutter is canceled via STAP individually in each range ambiguous region. A series of simulation results validate the efficacy of the proposed approach.

关键词： Clutter Radar Radar clutter signal processing algorithms Silicon Frequency modulation Doppler effect Vectors Object detection Minimization EPC-MIMO radar range-ambiguous clutter separation RANM STAP

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Online Learning of Expanding Graphs

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 247-255页

作者： Rey, Samuel Das, Bishwadeep Isufi, Elvin King Juan Carlos Univ Dept Signal Theory & Commun Madrid 28933 Spain Delft Univ Technol Intelligent Syst Dept NL-2628 CD Delft Netherlands

This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are crucial in delay-sensitive applications or when changes in topology occur rapidly. While existing works focus on inferring the connectivity within a fixed set of nodes, in practice, the graph can grow as new nodes join the network. This poses additional challenges like modeling temporal dynamics involving signals and graphs of different sizes. This growth also increases the computational complexity of the learning process, which may become prohibitive. To the best of our knowledge, this is the first work to tackle this setting. We propose a general online algorithm based on projected proximal gradient descent that accounts for the increasing graph size at each iteration. Recursively updating the sample covariance matrix is a key aspect of our approach. We introduce a strategy that enables different types of updates for nodes that just joined the network and for previously existing nodes. To provide further insights into the proposed method, we specialize it in Gaussian Markov random field settings, where we analyze the computational complexity and characterize the dynamic cumulative regret. Finally, we demonstrate the effectiveness of the proposed approach using both controlled experiments and real-world datasets from epidemic and financial networks.

关键词： Topology Network topology Heuristic algorithms signal processing algorithms signal processing Covariance matrices Mathematical models Inference algorithms Computational complexity Vectors Topology inference dynamic graph learning online graph learning expanding graphs graph signal processing

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An Open-Access Repository of Synchrophasor Data Quality Examples: Curation and Example Applications

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IEEE ACCESS 2025年 13卷 66445-66457页

作者： Biswas, Shuchismita Yin, Tianzhixi Naqvi, Syed Ahsan Raza Follum, Jim Varghese, Antos Cheeramban Ahmad, Tawsif Etingov, Pavel Pacific Northwest Natl Lab Richland WA 99354 USA

Synchrophasor measurements are critical in providing wide-area situational awareness to power system operators. However, data artifacts may be introduced in these measurements due to various factors such as loss of communication, loss of GPS signal, internal clock error, and vendor-specific implementation of phasor estimation algorithms. Tools designed to provide actionable insights from synchrophasor data, hence, must be designed to be robust to these data quality issues. To test the performance of algorithms in the presence of bad data encountered in field measurements, researchers need access to real-world examples of such data artifacts. To address this need, two years of synchrophasor data sourced from multiple electric utilities in the United States were analyzed to identify examples of data quality problems. These examples were then labeled and published in the Grid Event Signature Library, a publicly available repository of power system measurements hosted by the Oak Ridge National Laboratory. This paper describes the data curation process, and illustrates two application use cases where the dataset can be valuable to the research community. In the first use case, a random forest (RF) classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. With optimally tuned class weights, the RF classifier achieves an F1 score of 0.999. The second use case studies the impact of data quality issues on an example synchrophasor application (specifically, event start time determination). It is found that the introduction of quantization noise and intermittent data anomalies can increase the root mean square error for event start time determination by 1-4 seconds. The use cases demonstrate how the repository curated in this work can be valuable for developing and testing tools aimed at analyzing synchrophasor measurements.

关键词： Phasor measurement units Data integrity Quantization (signal) Libraries Voltage measurement Loss measurement signal processing algorithms Power measurement Noise Measurement uncertainty Phasor measurement unit time synchronization data quality data drops

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Charbonnier Quasi Hyperbolic Momentum Spline Based Incremental Strategy for Nonlinear Distributed Active Noise Control

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 1-15页

作者： Kranthi, Rajapantula Vasundhara Kar, Asutosh Christensen, Mads Grasboll Natl Inst Technol Dept Elect & Commun Engn Warangal 506004 India Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Elect & Commun Engn Jalandhar 144008 India Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark

Noise mitigation proves to be a challenging task for active noise control in the existence of nonlinearities. In such environments, functional link neural network (FLN) and adaptive exponential FLN techniques improve the performance of distributed active noise control systems. Nonlinear spline approaches are well known for their low computational complexity and ability to effectively alleviate noise in nonlinear systems. This paper proposes a new cost function for distributed active noise control (DANC) system which is based on the Charbonnier quasi hyperbolic momentum spline (CQHMS) involving incremental approach. This incremental based CQHMS DANC method employs Charbonnier loss and quasi hyperbolic momentum approach which minimizes gradient variance and local crossover points in order to enhance the convergence and steady-state performance. The technique being proposed demonstrates enhanced performance and achieves accelerated convergence when compared to existing techniques in a range of nonlinear DANC scenarios in lieu of varied nonlinear primary path and nonlinear secondary path conditions.

关键词： Noise Splines (mathematics) Microphones Convergence Adaptive systems Adaptation models Prevention and mitigation Loudspeakers Adaptive filters signal processing algorithms Charbonnier function nonlinear distributed ANC quasi hyperbolic momentum spline

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Robust Sequential Phase Estimation Using Multi-Temporal SAR Image Series

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IEEE signal processing LETTERS 2025年 32卷 811-815页

作者： El Hajjar, Dana Ginolhac, Guillaume Yan, Yajing El Korso, Mohammed Nabil Univ Savoie Mont Blanc LISTIC F-74000 Annecy France Univ Paris Saclay Cent Supelec L2S F-91190 Gif Sur Yvette France

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) exploits Synthetic Aperture Radar images time series (SAR-TS) for surface deformation monitoring via phase difference (with respect to a reference image) estimation. Most of the actual state-of-the-art MT-InSAR rely on temporal covariance matrix of the SAR-TS, assuming Gaussian distribution. However, these approaches become computationally expensive when the time series lengthens and new images are added to the data vector. This paper proposes a novel approach to sequentially integrate each newly acquired image using Phase Linking (PL) and Maximum Likelihood Estimation (MLE). The methodology divides the data into blocks, using previous images and estimations as a prior to sequentially estimate the phase of the new image. Actually, this framework allows to consider non Gaussian distributions, such as a mixture of scaled Gaussian distribution, which is particularly important to consider when dealing with urban areas.

关键词： Covariance matrices Coherence Synthetic aperture radar signal processing algorithms Radar polarimetry Maximum likelihood estimation Vectors Time series analysis Radar imaging Gaussian distribution Multi-temporal interferometric synthetic aperture radar maximum likelihood estimation covariance matrix estimation mixture of scaled Gaussian distribution

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