Pub Date : 2025-12-22DOI: 10.1109/TSP.2025.3646485
Hengyu Chen;Jiazhi Ma;Mengyuan Dong;Xitong Yang;Yongzhen Li
Polarimetric phased array radar (PPAR) has the capability to suppress mainlobe and sidelobe interferences by fully utilizing multiple polarization channels. However, adaptive beamforming technique for PPAR, which generates nulls in the space-polarization domain, causes the received signals from multiple polarization channels to be weighted into one. This results in an inherent loss of target polarization, preventing the measurement of target polarization scattering matrix (PSM). In this paper, a robust adaptive beamforming (RAB) approach for PPAR is proposed to estimate the PSM while suppressing the mainlobe and sidelobe interferences. In our solution, dual-beams characterized by a pair of optimal orthogonal polarizations are adaptively formed to reconstruct the polarization channels. Furthermore, multiple uncertainty sets are devised, including a spatial uncertainty set and a pair of polarization-associated uncertainty sets, which respectively improve performance in beam polarization stability and mainlobe interference suppression. This problem is then formulated as a non-convex quadratically constrained quadratic programming (QCQP), which is transformed into a difference of convex (DC) programming problem. Subsequently, it is efficiently solved via a sequential convex programming (SCP) algorithm, incorporating an initial point selection strategy. We further conduct a thorough performance analysis focusing on three critical aspects. As a result, the dual-beams can suppress mainlobe and sidelobe interferences in the space-polarization domain while estimating the target PSM accurately. Simulation results demonstrate the validity of the proposed method.
{"title":"Robust Adaptive Beamforming for Radar Target Polarization Scattering Matrix Estimation","authors":"Hengyu Chen;Jiazhi Ma;Mengyuan Dong;Xitong Yang;Yongzhen Li","doi":"10.1109/TSP.2025.3646485","DOIUrl":"10.1109/TSP.2025.3646485","url":null,"abstract":"Polarimetric phased array radar (PPAR) has the capability to suppress mainlobe and sidelobe interferences by fully utilizing multiple polarization channels. However, adaptive beamforming technique for PPAR, which generates nulls in the space-polarization domain, causes the received signals from multiple polarization channels to be weighted into one. This results in an inherent loss of target polarization, preventing the measurement of target polarization scattering matrix (PSM). In this paper, a robust adaptive beamforming (RAB) approach for PPAR is proposed to estimate the PSM while suppressing the mainlobe and sidelobe interferences. In our solution, dual-beams characterized by a pair of optimal orthogonal polarizations are adaptively formed to reconstruct the polarization channels. Furthermore, multiple uncertainty sets are devised, including a spatial uncertainty set and a pair of polarization-associated uncertainty sets, which respectively improve performance in beam polarization stability and mainlobe interference suppression. This problem is then formulated as a non-convex quadratically constrained quadratic programming (QCQP), which is transformed into a difference of convex (DC) programming problem. Subsequently, it is efficiently solved via a sequential convex programming (SCP) algorithm, incorporating an initial point selection strategy. We further conduct a thorough performance analysis focusing on three critical aspects. As a result, the dual-beams can suppress mainlobe and sidelobe interferences in the space-polarization domain while estimating the target PSM accurately. Simulation results demonstrate the validity of the proposed method.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"104-120"},"PeriodicalIF":5.8,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807780","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 : 2025-12-22DOI: 10.1109/TSP.2025.3646587
Enrico Grimaldi;Claudio Battiloro;Paolo Di Lorenzo
The aim of this paper is to introduce a novel dictionary learning algorithm for sparse representation of signals defined over combinatorial topological spaces, specifically, regular cell complexes. Leveraging Hodge theory, we embed topology into the dictionary structure via concatenated sub-dictionaries, each as a polynomial of Hodge Laplacians, yielding localized spectral topological filter frames. The learning problem is cast to jointly infer the underlying cell complex and optimize the dictionary coefficients and the sparse signal representation. We efficiently solve the problem via iterative alternating algorithms. Numerical results on both synthetic and real data show the effectiveness of the proposed procedure in jointly learning the sparse representations and the underlying relational structure of topological signals.
{"title":"Topological Dictionary Learning","authors":"Enrico Grimaldi;Claudio Battiloro;Paolo Di Lorenzo","doi":"10.1109/TSP.2025.3646587","DOIUrl":"10.1109/TSP.2025.3646587","url":null,"abstract":"The aim of this paper is to introduce a novel dictionary learning algorithm for sparse representation of signals defined over combinatorial topological spaces, specifically, regular cell complexes. Leveraging Hodge theory, we embed topology into the dictionary structure via concatenated sub-dictionaries, each as a polynomial of Hodge Laplacians, yielding localized spectral topological filter frames. The learning problem is cast to jointly infer the underlying cell complex and optimize the dictionary coefficients and the sparse signal representation. We efficiently solve the problem via iterative alternating algorithms. Numerical results on both synthetic and real data show the effectiveness of the proposed procedure in jointly learning the sparse representations and the underlying relational structure of topological signals.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"200-214"},"PeriodicalIF":5.8,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807778","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 : 2025-12-22DOI: 10.1109/TSP.2025.3647215
Jie Zhou;Junhao Xie;Jiaqi Chen
In this paper, we consider directly estimating the eigenvalues of precision matrix, without inverting the corresponding estimator for the eigenvalues of covariance matrix. We focus on a general asymptotic regime, i.e., the large dimensional regime, where both the dimension $N$ and the sample size $K$ tend to infinity whereas their quotient $N/K$ converges to a positive constant. By utilizing tools from random matrix theory, we construct an improved estimator for eigenvalues of precision matrix. We prove the consistency of the new estimator under large dimensional regime. In order to obtain the asymptotic bias term of the proposed estimator, we provide a theoretical result that characterizes the convergence rate of the expected Stieltjes transform (with its derivative) of the spectra of the sample covariance matrix. Using this result, we prove that the asymptotic bias term of the proposed estimator is of order $O(1/K^{2})$. Additionally, we establish a central limiting theorem (CLT) to describe the fluctuations of the new estimator. Finally, some numerical examples are presented to validate the excellent performance of the new estimator and to verify the accuracy of the CLT.
{"title":"Direct Estimation of Eigenvalues of Large Dimensional Precision Matrix","authors":"Jie Zhou;Junhao Xie;Jiaqi Chen","doi":"10.1109/TSP.2025.3647215","DOIUrl":"10.1109/TSP.2025.3647215","url":null,"abstract":"In this paper, we consider directly estimating the eigenvalues of precision matrix, without inverting the corresponding estimator for the eigenvalues of covariance matrix. We focus on a general asymptotic regime, i.e., the large dimensional regime, where both the dimension <inline-formula><tex-math>$N$</tex-math></inline-formula> and the sample size <inline-formula><tex-math>$K$</tex-math></inline-formula> tend to infinity whereas their quotient <inline-formula><tex-math>$N/K$</tex-math></inline-formula> converges to a positive constant. By utilizing tools from random matrix theory, we construct an improved estimator for eigenvalues of precision matrix. We prove the consistency of the new estimator under large dimensional regime. In order to obtain the asymptotic bias term of the proposed estimator, we provide a theoretical result that characterizes the convergence rate of the expected Stieltjes transform (with its derivative) of the spectra of the sample covariance matrix. Using this result, we prove that the asymptotic bias term of the proposed estimator is of order <inline-formula><tex-math>$O(1/K^{2})$</tex-math></inline-formula>. Additionally, we establish a central limiting theorem (CLT) to describe the fluctuations of the new estimator. Finally, some numerical examples are presented to validate the excellent performance of the new estimator and to verify the accuracy of the CLT.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"183-199"},"PeriodicalIF":5.8,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807779","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}
Cooperative Sensing Network (CSN) is an important system in broad applications such as Cognitive Radio Networks (CRNs) and Internet-of-Things (IoT). Recent advances in Over-the-Air (OTA) computation have enhanced CSN efficiency by significantly lowering communication overhead, allowing multiple sensing agents to simultaneously transmit over the same spectrum resource. However, OTA-based sensing networks can be vulnerable to Byzantine Attacks (BAs), and detailed analyses of their effects remain unaddressed. This paper formulates a general BA model for OTA-based CSN that aims to maximize the probability of false alarm ($P_{F}$) and minimize the probability of detection ($P_{D}$). We identify the most damaging BA strategy under the linear attack model and evaluate its impact on OTA-based CSN. To mitigate the impact of Byzantine attacks, we propose a direct defense mechanism by adapting the joint detection threshold to lower $P_{F}$ to a desired level. We present analyses to show how such defensive approaches weaken detection performance. These findings highlight the need for further developing advanced defense strategies against BAs in OTA-based collaborative networks.
{"title":"Byzantine Attacks in Over-the-Air Cooperative Sensing Networks: Analysis and Defense","authors":"Weiwei Wang;Vincent Huynh;Carlos Feres;Lifeng Lai;Zhi Ding","doi":"10.1109/TSP.2025.3646135","DOIUrl":"10.1109/TSP.2025.3646135","url":null,"abstract":"Cooperative Sensing Network (CSN) is an important system in broad applications such as Cognitive Radio Networks (CRNs) and Internet-of-Things (IoT). Recent advances in Over-the-Air (OTA) computation have enhanced CSN efficiency by significantly lowering communication overhead, allowing multiple sensing agents to simultaneously transmit over the same spectrum resource. However, OTA-based sensing networks can be vulnerable to Byzantine Attacks (BAs), and detailed analyses of their effects remain unaddressed. This paper formulates a general BA model for OTA-based CSN that aims to maximize the probability of false alarm (<inline-formula><tex-math>$P_{F}$</tex-math></inline-formula>) and minimize the probability of detection (<inline-formula><tex-math>$P_{D}$</tex-math></inline-formula>). We identify the most damaging BA strategy under the linear attack model and evaluate its impact on OTA-based CSN. To mitigate the impact of Byzantine attacks, we propose a direct defense mechanism by adapting the joint detection threshold to lower <inline-formula><tex-math>$P_{F}$</tex-math></inline-formula> to a desired level. We present analyses to show how such defensive approaches weaken detection performance. These findings highlight the need for further developing advanced defense strategies against BAs in OTA-based collaborative networks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"75-87"},"PeriodicalIF":5.8,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807781","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 : 2025-12-19DOI: 10.1109/TSP.2025.3646236
Morad Halihal;Tirza Routtenberg;H. Vincent Poor
In this paper, we analyze the performance of the estimation of Laplacian matrices under general observation models. Laplacian matrix estimation involves structural constraints, including symmetry and null-space properties, along with matrix sparsity. By exploiting a linear reparametrization that enforces the structural constraints, we derive closed-form matrix expressions for the Cram$acute{text{e}}$r-Rao bound (CRB) specifically tailored to Laplacian matrix estimation. We further extend the derivation to the sparsity-constrained case, introducing two oracle CRBs that incorporate prior information of the support set, i.e. the locations of the nonzero entries in the Laplacian matrix. We examine the properties and order relations between the bounds, and provide the associated Slepian-Bangs formula for the Gaussian case. We demonstrate the use of the new CRBs in three representative applications: (i) topology identification in power systems, (ii) graph filter identification in diffused models, and (iii) precision matrix estimation in Gaussian Markov random fields under Laplacian constraints. The CRBs are evaluated and compared with the mean-squared-errors (MSEs) of the constrained maximum likelihood estimator (CMLE), which integrates both equality and inequality constraints along with sparsity constraints, and of the oracle CMLE, which knows the locations of the nonzero entries of the Laplacian matrix. We perform this analysis for the applications of power system topology identification and graphical LASSO, and demonstrate that the MSEs of the estimators converge to the CRB and oracle CRB, given a sufficient number of measurements.
{"title":"Cram$acute{text{e}}$r-Rao Bounds for Laplacian Matrix Estimation","authors":"Morad Halihal;Tirza Routtenberg;H. Vincent Poor","doi":"10.1109/TSP.2025.3646236","DOIUrl":"10.1109/TSP.2025.3646236","url":null,"abstract":"In this paper, we analyze the performance of the estimation of Laplacian matrices under general observation models. Laplacian matrix estimation involves structural constraints, including symmetry and null-space properties, along with matrix sparsity. By exploiting a linear reparametrization that enforces the structural constraints, we derive closed-form matrix expressions for the Cram<inline-formula><tex-math>$acute{text{e}}$</tex-math></inline-formula>r-Rao bound (CRB) specifically tailored to Laplacian matrix estimation. We further extend the derivation to the sparsity-constrained case, introducing two oracle CRBs that incorporate prior information of the support set, i.e. the locations of the nonzero entries in the Laplacian matrix. We examine the properties and order relations between the bounds, and provide the associated Slepian-Bangs formula for the Gaussian case. We demonstrate the use of the new CRBs in three representative applications: (i) topology identification in power systems, (ii) graph filter identification in diffused models, and (iii) precision matrix estimation in Gaussian Markov random fields under Laplacian constraints. The CRBs are evaluated and compared with the mean-squared-errors (MSEs) of the constrained maximum likelihood estimator (CMLE), which integrates both equality and inequality constraints along with sparsity constraints, and of the oracle CMLE, which knows the locations of the nonzero entries of the Laplacian matrix. We perform this analysis for the applications of power system topology identification and graphical LASSO, and demonstrate that the MSEs of the estimators converge to the CRB and oracle CRB, given a sufficient number of measurements.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"88-103"},"PeriodicalIF":5.8,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785598","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}
{"title":"Ziv-Zakai Bound for DOAs Estimation under Arbitrary-bit Quantization","authors":"Zongyu Zhang, Zhiguo Shi, Jiming Chen, Maria Sabrina Greco, Fulvio Gini, Yujie Gu","doi":"10.1109/tsp.2025.3639025","DOIUrl":"https://doi.org/10.1109/tsp.2025.3639025","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"1 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778015","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 : 2025-12-17DOI: 10.1109/TSP.2025.3645929
Jing Yang;Tao Fan;Xianxiang Yu;Zhengyu Zhu;Chenguang Shi;Yangyang Dong;Guolong Cui
This paper deals with the dual function waveform design problem for a knowledge-aided integrated radar and jamming (IRAJ) system. Supposing the IRAJ system has access to an information database obtained from a reconnaissance system for the threatening radar’s transmit waveform knowledge, the signal-to-jamming and noise ratio (SJNR) at the output of the threatening radar filter is considered as a figure of merit to reduce its detection probability, which represents the performance of blanket jamming. Besides, along with energy and peak-to-average ratio (PAR) constraints to comply with the hardware realization, let us minimize the weighted peak sidelobe level (WPSL) or maximize the signal-to-interference and noise ratio (SINR) of the IRAJ system for the detection performance according to the delay structure of reflected echoes. To tackle the resulting non-convex multi-objective optimization problems, iterative fractional programming algorithms (IFPA) leveraging cyclic algorithm-new (CAN) and alternating direction method of multipliers (ADMM) are proposed, respectively. Finally, simulation results are provided to demonstrate the competition between radar and jamming functions within the proposed Pareto optimization framework and validate the effectiveness of the conceived algorithms.
{"title":"Knowledge-Aided Integrated Radar and Jamming Waveform Design via Iterative Fractional Programming Algorithm","authors":"Jing Yang;Tao Fan;Xianxiang Yu;Zhengyu Zhu;Chenguang Shi;Yangyang Dong;Guolong Cui","doi":"10.1109/TSP.2025.3645929","DOIUrl":"10.1109/TSP.2025.3645929","url":null,"abstract":"This paper deals with the dual function waveform design problem for a knowledge-aided integrated radar and jamming (IRAJ) system. Supposing the IRAJ system has access to an information database obtained from a reconnaissance system for the threatening radar’s transmit waveform knowledge, the signal-to-jamming and noise ratio (SJNR) at the output of the threatening radar filter is considered as a figure of merit to reduce its detection probability, which represents the performance of blanket jamming. Besides, along with energy and peak-to-average ratio (PAR) constraints to comply with the hardware realization, let us minimize the weighted peak sidelobe level (WPSL) or maximize the signal-to-interference and noise ratio (SINR) of the IRAJ system for the detection performance according to the delay structure of reflected echoes. To tackle the resulting non-convex multi-objective optimization problems, iterative fractional programming algorithms (IFPA) leveraging cyclic algorithm-new (CAN) and alternating direction method of multipliers (ADMM) are proposed, respectively. Finally, simulation results are provided to demonstrate the competition between radar and jamming functions within the proposed Pareto optimization framework and validate the effectiveness of the conceived algorithms.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"137-149"},"PeriodicalIF":5.8,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770881","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 : 2025-12-17DOI: 10.1109/TSP.2025.3645629
Zhen Qin;Zhihui Zhu
As intelligent reflecting surface (IRS) has emerged as a new and promising technology capable of configuring the wireless environment favorably, channel estimation for IRS-assisted multiple-input multiple-output (MIMO) systems has garnered extensive attention in recent years. Despite the development of numerous algorithms to address this challenge, a comprehensive theoretical characterization of the optimal recovery error is still lacking. This paper aims to address this gap by providing theoretical guarantees in terms of stable recovery of channel matrices for noisy measurements. We begin by establishing the equivalence between IRS-assisted MIMO systems in the uplink scenario and a compact tensor train (TT)-based tensor-on-tensor (ToT) regression. Building on this equivalence, we then investigate the restricted isometry property (RIP) for complex-valued subgaussian measurements. Our analysis reveals that successful recovery hinges on the relationship between the number of user terminals and the number of time slots during which channel matrices remain invariant. Utilizing the RIP condition, we establish a theoretical upper bound on the recovery error for solutions to the constrained least-squares optimization problem, as well as a minimax lower bound for the considered model. Our analysis demonstrates that the recovery error decreases inversely with the number of time slots, and increases proportionally with the total number of unknown entries in the channel matrices, thereby quantifying the fundamental trade-offs in channel estimation accuracy. In addition, we explore a multi-hop IRS scheme and analyze the corresponding recovery errors. Finally, we have performed numerical experiments to support our theoretical findings.
{"title":"Optimal Error Analysis of Channel Estimation for IRS-Assisted MIMO Systems","authors":"Zhen Qin;Zhihui Zhu","doi":"10.1109/TSP.2025.3645629","DOIUrl":"10.1109/TSP.2025.3645629","url":null,"abstract":"As intelligent reflecting surface (IRS) has emerged as a new and promising technology capable of configuring the wireless environment favorably, channel estimation for IRS-assisted multiple-input multiple-output (MIMO) systems has garnered extensive attention in recent years. Despite the development of numerous algorithms to address this challenge, a comprehensive theoretical characterization of the optimal recovery error is still lacking. This paper aims to address this gap by providing theoretical guarantees in terms of stable recovery of channel matrices for noisy measurements. We begin by establishing the equivalence between IRS-assisted MIMO systems in the uplink scenario and a compact tensor train (TT)-based tensor-on-tensor (ToT) regression. Building on this equivalence, we then investigate the restricted isometry property (RIP) for complex-valued subgaussian measurements. Our analysis reveals that successful recovery hinges on the relationship between the number of user terminals and the number of time slots during which channel matrices remain invariant. Utilizing the RIP condition, we establish a theoretical upper bound on the recovery error for solutions to the constrained least-squares optimization problem, as well as a minimax lower bound for the considered model. Our analysis demonstrates that the recovery error decreases inversely with the number of time slots, and increases proportionally with the total number of unknown entries in the channel matrices, thereby quantifying the fundamental trade-offs in channel estimation accuracy. In addition, we explore a multi-hop IRS scheme and analyze the corresponding recovery errors. Finally, we have performed numerical experiments to support our theoretical findings.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"61-74"},"PeriodicalIF":5.8,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770889","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 : 2025-12-17DOI: 10.1109/tsp.2025.3644686
Marco Carpentiero, Virginia Bordignon, Vincenzo Matta, Ali H. Sayed
{"title":"Doubly Adaptive Social Learning","authors":"Marco Carpentiero, Virginia Bordignon, Vincenzo Matta, Ali H. Sayed","doi":"10.1109/tsp.2025.3644686","DOIUrl":"https://doi.org/10.1109/tsp.2025.3644686","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"30 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770883","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}
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