Pub Date : 2025-12-03DOI: 10.1109/TSP.2025.3638366
Xiaoli Xu;Zhiwen Zhou;Yong Zeng
Orthogonal frequency division multiplexing (OFDM), which has been the dominant waveform for contemporary wireless communications, is also regarded as a competitive candidate for future integrated sensing and communication (ISAC) systems. Existing work on OFDM-ISAC usually assumes that the maximum sensing range should be limited by the cyclic prefix (CP) length since inter-symbol interference (ISI) and inter-carrier interference (ICI) should be avoided. However, in this paper, we provide a rigorous analysis to reveal that the random data embedded in the OFDM-ISAC signal can actually act as a free “mask” for ISI, which makes ISI/ICI random and hence greatly attenuated after radar signal processing. The derived signal-to-interference-plus-noise ratio (SINR) in the range profile demonstrates that the maximum sensing range of OFDM-ISAC can greatly exceed the ISI-free distance that is limited by the CP length, which is also validated by simulation results. To further mitigate power degradation for long-range targets, a novel sliding window sensing method is proposed, which iteratively detects and cancels short-range targets before shifting the detection window. The shifted detection window can effectively compensate for the power degradation due to insufficient CP length for long-range targets. Such results provide valuable guidance for the design of the CP length in OFDM-ISAC systems.
{"title":"How Does CP Length Affect the Sensing Range for OFDM-ISAC?","authors":"Xiaoli Xu;Zhiwen Zhou;Yong Zeng","doi":"10.1109/TSP.2025.3638366","DOIUrl":"10.1109/TSP.2025.3638366","url":null,"abstract":"Orthogonal frequency division multiplexing (OFDM), which has been the dominant waveform for contemporary wireless communications, is also regarded as a competitive candidate for future integrated sensing and communication (ISAC) systems. Existing work on OFDM-ISAC usually assumes that the maximum sensing range should be limited by the cyclic prefix (CP) length since inter-symbol interference (ISI) and inter-carrier interference (ICI) should be avoided. However, in this paper, we provide a rigorous analysis to reveal that the random data embedded in the OFDM-ISAC signal can actually act as a free “mask” for ISI, which makes ISI/ICI random and hence greatly attenuated after radar signal processing. The derived signal-to-interference-plus-noise ratio (SINR) in the range profile demonstrates that the maximum sensing range of OFDM-ISAC can greatly exceed the ISI-free distance that is limited by the CP length, which is also validated by simulation results. To further mitigate power degradation for long-range targets, a novel sliding window sensing method is proposed, which iteratively detects and cancels short-range targets before shifting the detection window. The shifted detection window can effectively compensate for the power degradation due to insufficient CP length for long-range targets. Such results provide valuable guidance for the design of the CP length in OFDM-ISAC systems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"5106-5120"},"PeriodicalIF":5.8,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664749","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}
Functional tensor decomposition can analyze multi-dimensional data with real-valued indices, paving the path for applications in machine learning and signal processing. A limitation of existing approaches is the assumption that the tensor rank—a critical parameter governing model complexity—is known. However, determining the optimal rank is a non-deterministic polynomial-time hard (NP-hard) task and there is a limited understanding regarding the expressive power of functional low-rank tensor models for continuous signals. We propose a rank-revealing functional Bayesian tensor completion (RR-FBTC) method. Modeling the latent functions through carefully designed multioutput Gaussian processes, RR-FBTC handles tensors with real-valued indices while enabling automatic tensor rank determination during the inference process. We establish the universal approximation property of the model for continuous multi-dimensional signals, demonstrating its expressive power in a concise format. To learn this model, we employ the variational inference framework and derive an efficient algorithm with closed-form updates. Experiments on both synthetic and real-world datasets demonstrate the effectiveness and superiority of the RR-FBTC over state-of-the-art approaches. The code is available at https://github.com/OceanSTARLab/RR-FBTC.
{"title":"When Bayesian Tensor Completion Meets Multioutput Gaussian Processes: Functional Universality and Rank Learning","authors":"Siyuan Li;Shikai Fang;Lei Cheng;Feng Yin;Yik-Chung Wu;Peter Gerstoft;Sergios Theodoridis","doi":"10.1109/TSP.2025.3639229","DOIUrl":"10.1109/TSP.2025.3639229","url":null,"abstract":"Functional tensor decomposition can analyze multi-dimensional data with real-valued indices, paving the path for applications in machine learning and signal processing. A limitation of existing approaches is the assumption that the tensor rank—a critical parameter governing model complexity—is known. However, determining the optimal rank is a non-deterministic polynomial-time hard (NP-hard) task and there is a limited understanding regarding the expressive power of functional low-rank tensor models for continuous signals. We propose a rank-revealing functional Bayesian tensor completion (RR-FBTC) method. Modeling the latent functions through carefully designed multioutput Gaussian processes, RR-FBTC handles tensors with real-valued indices while enabling automatic tensor rank determination during the inference process. We establish the universal approximation property of the model for continuous multi-dimensional signals, demonstrating its expressive power in a concise format. To learn this model, we employ the variational inference framework and derive an efficient algorithm with closed-form updates. Experiments on both synthetic and real-world datasets demonstrate the effectiveness and superiority of the RR-FBTC over state-of-the-art approaches. The code is available at <uri>https://github.com/OceanSTARLab/RR-FBTC</uri>.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"5319-5335"},"PeriodicalIF":5.8,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664757","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 : 2025-12-02DOI: 10.1109/TSP.2025.3638774
Peishi Li;Ming Li;Rang Liu;Qian Liu;A. Lee Swindlehurst
Orthogonal frequency division multiplexing - integrated sensing and communication (OFDM-ISAC) has emerged as a key enabler for future wireless networks, leveraging the widely adopted OFDM waveform to seamlessly integrate wireless communication and radar sensing within a unified framework. In this paper, we propose adaptive resource allocation strategies for OFDM-ISAC systems to achieve optimal trade-offs between diverse sensing requirements and communication quality-of-service (QoS). We first develop a comprehensive resource allocation framework for OFDM-ISAC systems, deriving closed-form expressions for key sensing performance metrics including delay resolution, Doppler resolution, delay-Doppler peak sidelobe level (PSL), and sensing signal-to-noise ratio (SNR). Building on this theoretical foundation, we introduce two novel resource allocation algorithms tailored to distinct sensing objectives. The resolution-oriented algorithm aims to maximize the weighted delay-Doppler resolution while satisfying constraints on PSL, sensing SNR, communication sum-rate, and transmit power. The sidelobe-oriented algorithm focuses on minimizing delay-Doppler PSL while satisfying resolution, SNR, and communication constraints. To efficiently solve the resulting non-convex optimization problems, we develop two adaptive resource allocation algorithms based on Dinkelbach’s transform and majorization-minimization (MM). Extensive simulations validate the effectiveness of the proposed sensing-oriented adaptive resource allocation strategies in enhancing resolution and sidelobe suppression. Remarkably, these strategies achieve sensing performance nearly identical to that of a radar-only scheme, which dedicates all resources to sensing. These results highlight the superior performance of the proposed methods in optimizing the trade-off between sensing and communication objectives within OFDM-ISAC systems.
{"title":"Sensing-Oriented Adaptive Resource Allocation Designs for OFDM-ISAC Systems","authors":"Peishi Li;Ming Li;Rang Liu;Qian Liu;A. Lee Swindlehurst","doi":"10.1109/TSP.2025.3638774","DOIUrl":"10.1109/TSP.2025.3638774","url":null,"abstract":"Orthogonal frequency division multiplexing - integrated sensing and communication (OFDM-ISAC) has emerged as a key enabler for future wireless networks, leveraging the widely adopted OFDM waveform to seamlessly integrate wireless communication and radar sensing within a unified framework. In this paper, we propose adaptive resource allocation strategies for OFDM-ISAC systems to achieve optimal trade-offs between diverse sensing requirements and communication quality-of-service (QoS). We first develop a comprehensive resource allocation framework for OFDM-ISAC systems, deriving closed-form expressions for key sensing performance metrics including delay resolution, Doppler resolution, delay-Doppler peak sidelobe level (PSL), and sensing signal-to-noise ratio (SNR). Building on this theoretical foundation, we introduce two novel resource allocation algorithms tailored to distinct sensing objectives. The resolution-oriented algorithm aims to maximize the weighted delay-Doppler resolution while satisfying constraints on PSL, sensing SNR, communication sum-rate, and transmit power. The sidelobe-oriented algorithm focuses on minimizing delay-Doppler PSL while satisfying resolution, SNR, and communication constraints. To efficiently solve the resulting non-convex optimization problems, we develop two adaptive resource allocation algorithms based on Dinkelbach’s transform and majorization-minimization (MM). Extensive simulations validate the effectiveness of the proposed sensing-oriented adaptive resource allocation strategies in enhancing resolution and sidelobe suppression. Remarkably, these strategies achieve sensing performance nearly identical to that of a radar-only scheme, which dedicates all resources to sensing. These results highlight the superior performance of the proposed methods in optimizing the trade-off between sensing and communication objectives within OFDM-ISAC systems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"5121-5135"},"PeriodicalIF":5.8,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665021","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 : 2025-12-02DOI: 10.1109/TSP.2025.3639067
Haoming Liu;Chung-Yiu Yau;Hoi-To Wai
This paper introduces a robust two-timescale compressed primal-dual (TiCoPD) algorithm tailored for decentralized optimization under bandwidth-limited and unreliable channels. By integrating the majorization-minimization approach with the primal-dual optimization framework, the TiCoPD algorithm strategically compresses the difference term shared among agents to enhance communication efficiency and robustness against noisy channels without compromising convergence stability. The method incorporates a mirror sequence for agent consensus on nonlinearly compressed terms updated on a fast timescale, together with a slow timescale primal-dual recursion for optimizing the objective function. Our analysis demonstrates that the proposed algorithm converges to a stationary solution when the objective function is smooth but possibly non-convex. Numerical experiments corroborate the conclusions of this paper.
{"title":"Decentralized Stochastic Optimization Over Unreliable Networks via Two-Timescales Updates","authors":"Haoming Liu;Chung-Yiu Yau;Hoi-To Wai","doi":"10.1109/TSP.2025.3639067","DOIUrl":"10.1109/TSP.2025.3639067","url":null,"abstract":"This paper introduces a robust two-timescale compressed primal-dual (TiCoPD) algorithm tailored for decentralized optimization under bandwidth-limited and unreliable channels. By integrating the majorization-minimization approach with the primal-dual optimization framework, the TiCoPD algorithm strategically compresses the difference term shared among agents to enhance communication efficiency and robustness against noisy channels without compromising convergence stability. The method incorporates a mirror sequence for agent consensus on nonlinearly compressed terms updated on a fast timescale, together with a slow timescale primal-dual recursion for optimizing the objective function. Our analysis demonstrates that the proposed algorithm converges to a stationary solution when the objective function is smooth but possibly non-convex. Numerical experiments corroborate the conclusions of this paper.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"74 ","pages":"121-136"},"PeriodicalIF":5.8,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664758","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 : 2025-12-01DOI: 10.1109/TSP.2025.3637157
Yile Xing;Donglong Chen;Hong Yan;Ray C.C. Cheung
Convolution is fundamental in digital signal processing across many applications. Existing works enable $ N $-point linear convolution via $ N $-point right-angle circular convolution (RCC) based on weighted transforms, effectively removing the need for zero padding. However, these methods are constrained by their choice of weights, which impacts the complexity of weight-related multiplications at the start of the transform and the end of the inverse transform. This limitation leads to either reduced accuracy or increased complexity in weighted Fourier transform (WFT)-based convolution, as well as restricted transform lengths in weighted Fermat number transform (WFNT)-based convolution. In this work, we address these challenges by merging the multiplications by weights into the butterfly structures with arbitrary power-of-2 radix for both WFT and WFNT. We also propose an extraction method to accommodate negative and complex numbers. Our work ensures that weights do not increase complexity, thereby improving accuracy and reducing complexity in WFT-based convolution, while allowing for a broader range of transform lengths in WFNT-based convolution.
{"title":"Fast Weighted Transforms for Linear Convolution","authors":"Yile Xing;Donglong Chen;Hong Yan;Ray C.C. Cheung","doi":"10.1109/TSP.2025.3637157","DOIUrl":"10.1109/TSP.2025.3637157","url":null,"abstract":"Convolution is fundamental in digital signal processing across many applications. Existing works enable <inline-formula> <tex-math>$ N $</tex-math> </inline-formula>-point linear convolution via <inline-formula> <tex-math>$ N $</tex-math> </inline-formula>-point right-angle circular convolution (RCC) based on weighted transforms, effectively removing the need for zero padding. However, these methods are constrained by their choice of weights, which impacts the complexity of weight-related multiplications at the start of the transform and the end of the inverse transform. This limitation leads to either reduced accuracy or increased complexity in weighted Fourier transform (WFT)-based convolution, as well as restricted transform lengths in weighted Fermat number transform (WFNT)-based convolution. In this work, we address these challenges by merging the multiplications by weights into the butterfly structures with arbitrary power-of-2 radix for both WFT and WFNT. We also propose an extraction method to accommodate negative and complex numbers. Our work ensures that weights do not increase complexity, thereby improving accuracy and reducing complexity in WFT-based convolution, while allowing for a broader range of transform lengths in WFNT-based convolution.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"5003-5014"},"PeriodicalIF":5.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145664794","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 : 2025-11-27DOI: 10.1109/TSP.2025.3637549
Yao Li;Jiawang Zhao;Zhichao Zhang;Xue Zhang;Xinping Guan
In this paper, we investigate a power-constrained optimal denial-of-service (DoS) attack schedule against transmission over interactive wireless sensor networks. A novel scenario is considered in which an attacker aims to attack the transmission links of the interactive networks to maximize the global error covariance with constrained energy budget. The attacker decides whether to launch an attack or not at each time slot. Two different cost functions are considered, i.e., terminal-error case and average-error case. Through decoupling analysis, the global error covariance can be separated into two portions: one part generated by the initial error, and the other by system noise. Based on this separation, the analytical solution for the terminal-error case is clearly derived. For the average-error case, the indices “occurrence” and “concentration” are introduced. These two indices are used to quantitatively describe the number of times a certain operator appears in a set of error terms generated by iteration, and the accumulative effect of attacks over a time scale, respectively. Aided by these two proposed indices, the optimal attack schedule for the average-error case is analytically derived. Additionally, a problem with regard to the maximization of occurrence is proposed, whose result can be proved to share the same form as the optimal attack schedule for average-error case with measurement noise. Moreover, we have extended our theoretical results to those of distributed scenarios, which follow the similar temporal distributions of global ones. Numerical simulations, including comparison with the learning-based state-of-the-art method, have demonstrated the correctness and effectiveness of our proposed scheduling strategies.
{"title":"Energy-Efficient Optimal Denial-of-Service Attack Schedule Against Transmission Over Interactive Networks","authors":"Yao Li;Jiawang Zhao;Zhichao Zhang;Xue Zhang;Xinping Guan","doi":"10.1109/TSP.2025.3637549","DOIUrl":"10.1109/TSP.2025.3637549","url":null,"abstract":"In this paper, we investigate a power-constrained optimal denial-of-service (DoS) attack schedule against transmission over interactive wireless sensor networks. A novel scenario is considered in which an attacker aims to attack the transmission links of the interactive networks to maximize the global error covariance with constrained energy budget. The attacker decides whether to launch an attack or not at each time slot. Two different cost functions are considered, i.e., terminal-error case and average-error case. Through decoupling analysis, the global error covariance can be separated into two portions: one part generated by the initial error, and the other by system noise. Based on this separation, the analytical solution for the terminal-error case is clearly derived. For the average-error case, the indices “<italic>occurrence</i>” and “<italic>concentration</i>” are introduced. These two indices are used to quantitatively describe the number of times a certain operator appears in a set of error terms generated by iteration, and the accumulative effect of attacks over a time scale, respectively. Aided by these two proposed indices, the optimal attack schedule for the average-error case is analytically derived. Additionally, a problem with regard to the maximization of <italic>occurrence</i> is proposed, whose result can be proved to share the same form as the optimal attack schedule for average-error case with measurement noise. Moreover, we have extended our theoretical results to those of distributed scenarios, which follow the similar temporal distributions of global ones. Numerical simulations, including comparison with the learning-based state-of-the-art method, have demonstrated the correctness and effectiveness of our proposed scheduling strategies.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"5168-5182"},"PeriodicalIF":5.8,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610981","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 : 2025-11-26DOI: 10.1109/TSP.2025.3637317
Niclas Führling;Giuseppe Thadeu Freitas de Abreu;David González G.;Osvaldo Gonsa
We consider a robust and self-reliant (or “egoistic”) variation of the rigid body localization (RBL) problem, in which a primary rigid body seeks to estimate the pose ($i.e.$, location and orientation) of another rigid body (or “target”), relative to its own, without the assistance of external infrastructure, without prior knowledge of the shape of the target, and taking into account the possibility that the available observations are incomplete. Three complementary contributions are then offered for such a scenario. The first is a method to estimate the translation vector between the center points of both rigid bodies, which unlike existing techniques does not require that both objects have the same shape or even the same number of landmark points. This technique is shown to significantly outperform the state-of-the-art (SotA) under complete information, but to be sensitive to data erasures, even when enhanced by matrix completion methods. The second contribution, designed to offer improved performance in the presence of incomplete information, offers a robust alternative to the latter, at the expense of a slight relative loss under complete information. Finally, the third contribution is a scheme for the estimation of the rotation matrix describing the relative orientation of the target rigid body with respect to the primary. Comparisons of the proposed schemes and SotA techniques demonstrate the advantage of the contributed methods in terms of root mean square error (RMSE) performance under fully complete information and incomplete conditions.
{"title":"Robust Egoistic Rigid Body Localization","authors":"Niclas Führling;Giuseppe Thadeu Freitas de Abreu;David González G.;Osvaldo Gonsa","doi":"10.1109/TSP.2025.3637317","DOIUrl":"10.1109/TSP.2025.3637317","url":null,"abstract":"We consider a robust and self-reliant (or “egoistic”) variation of the rigid body localization (RBL) problem, in which a primary rigid body seeks to estimate the pose (<inline-formula><tex-math>$i.e.$</tex-math></inline-formula>, location and orientation) of another rigid body (or “target”), relative to its own, without the assistance of external infrastructure, without prior knowledge of the shape of the target, and taking into account the possibility that the available observations are incomplete. Three complementary contributions are then offered for such a scenario. The first is a method to estimate the translation vector between the center points of both rigid bodies, which unlike existing techniques does not require that both objects have the same shape or even the same number of landmark points. This technique is shown to significantly outperform the state-of-the-art (SotA) under complete information, but to be sensitive to data erasures, even when enhanced by matrix completion methods. The second contribution, designed to offer improved performance in the presence of incomplete information, offers a robust alternative to the latter, at the expense of a slight relative loss under complete information. Finally, the third contribution is a scheme for the estimation of the rotation matrix describing the relative orientation of the target rigid body with respect to the primary. Comparisons of the proposed schemes and SotA techniques demonstrate the advantage of the contributed methods in terms of root mean square error (RMSE) performance under fully complete information and incomplete conditions.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"5076-5089"},"PeriodicalIF":5.8,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609347","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 : 2025-11-26DOI: 10.1109/TSP.2025.3637278
Hongqi Min;Xinrui Li;Ruoguang Li;Yong Zeng
For the sixth generation (6G) wireless networks, achieving high-performance integrated localization and communication (ILAC) is critical to unlock the full potential of wireless networks. To simultaneously enhance wireless localization and communication performance cost-effectively, this paper proposes sparse multiple-input multiple-output (MIMO) based ILAC with nested and co-prime sparse arrays deployed at the base station (BS). Sparse MIMO relaxes the traditional half-wavelength antenna spacing constraint to enlarge the antenna aperture, thus enhancing localization degrees of freedom (DoFs) and providing finer spatial resolution. However, it also leads to undesired grating lobes, which may cause severe inter-user interference (IUI) for communication and angular ambiguity for localization. While the latter issue can be effectively addressed by the virtual array technology, by forming sum or difference co-arrays via signal (conjugate) correlation among array elements, it is unclear whether the similar virtual array technology also benefits wireless communications for ILAC systems. In this paper, we first reveal that the answer to the above question is negative, by showing that forming virtual arrays for wireless communication will cause destruction of phase information, degradation of signal-to-noise ratio and aggravation of multi-user interference. Therefore, we propose the so-called hybrid processing framework for sparse MIMO based ILAC, i.e., physical array based communication while virtual array based localization. To this end, we characterize the beam pattern of sparse arrays by three metrics, i.e., main lobe beam width, peak-to-local-minimum ratio, and side lobe height, demonstrating that despite of the undesired grating lobes, sparse arrays can also bring benefits to communications, thanks to its narrower main lobe beam width than the conventional compact arrays. Extensive simulation results are presented to demonstrate the performance gains of sparse MIMO based ILAC over that based on the conventional compact MIMO.
{"title":"Integrated Localization and Communication With Sparse MIMO: Will Virtual Array Technology Also Benefit Wireless Communication?","authors":"Hongqi Min;Xinrui Li;Ruoguang Li;Yong Zeng","doi":"10.1109/TSP.2025.3637278","DOIUrl":"10.1109/TSP.2025.3637278","url":null,"abstract":"For the sixth generation (6G) wireless networks, achieving high-performance integrated localization and communication (ILAC) is critical to unlock the full potential of wireless networks. To simultaneously enhance wireless localization and communication performance cost-effectively, this paper proposes sparse multiple-input multiple-output (MIMO) based ILAC with nested and co-prime sparse arrays deployed at the base station (BS). Sparse MIMO relaxes the traditional half-wavelength antenna spacing constraint to enlarge the antenna aperture, thus enhancing localization degrees of freedom (DoFs) and providing finer spatial resolution. However, it also leads to undesired grating lobes, which may cause severe inter-user interference (IUI) for communication and angular ambiguity for localization. While the latter issue can be effectively addressed by the virtual array technology, by forming sum or difference co-arrays via signal (conjugate) correlation among array elements, it is unclear whether the similar virtual array technology also benefits wireless communications for ILAC systems. In this paper, we first reveal that the answer to the above question is negative, by showing that forming virtual arrays for wireless communication will cause destruction of phase information, degradation of signal-to-noise ratio and aggravation of multi-user interference. Therefore, we propose the so-called hybrid processing framework for sparse MIMO based ILAC, i.e., physical array based communication while virtual array based localization. To this end, we characterize the beam pattern of sparse arrays by three metrics, i.e., main lobe beam width, peak-to-local-minimum ratio, and side lobe height, demonstrating that despite of the undesired grating lobes, sparse arrays can also bring benefits to communications, thanks to its narrower main lobe beam width than the conventional compact arrays. Extensive simulation results are presented to demonstrate the performance gains of sparse MIMO based ILAC over that based on the conventional compact MIMO.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"5090-5105"},"PeriodicalIF":5.8,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609421","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 : 2025-11-24DOI: 10.1109/tsp.2025.3636071
Sergio Rozada, Hoi-To Wai, Antonio G. Marques
{"title":"Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement Learning","authors":"Sergio Rozada, Hoi-To Wai, Antonio G. Marques","doi":"10.1109/tsp.2025.3636071","DOIUrl":"https://doi.org/10.1109/tsp.2025.3636071","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"14 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593242","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 : 2025-11-21DOI: 10.1109/TSP.2025.3632844
Hao Shu;Jicheng Li;Yu Jin;Hailin Wang
In recent years, the prediction of multidimensional time series data has become increasingly important due to its wide-ranging applications. Tensor-based prediction methods have gained attention for their ability to preserve the inherent structure of such data. However, existing approaches, such as tensor autoregression and tensor decomposition, often have consistently failed to provide clear assertions regarding the number of samples that can be exactly predicted. While matrix-based methods using nuclear norms address this limitation, their reliance on matrices limits accuracy and increases computational costs when handling multidimensional data. To overcome these challenges, we reformulate multidimensional time series prediction as a deterministic tensor completion problem and propose a novel theoretical framework. Specifically, we develop a deterministic tensor completion theory and introduce the Temporal Convolutional Tensor Nuclear Norm (TCTNN) model. By convolving the multidimensional time series along the temporal dimension and applying the tensor nuclear norm, our approach identifies the maximum forecast horizon for exact predictions. Additionally, TCTNN achieves superior performance in prediction accuracy and computational efficiency compared to existing methods across diverse real-world datasets, including climate temperature, network flow, and traffic ride data. Our implementation is publicly available at https://github.com/HaoShu2000/TCTNN.
{"title":"Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theory","authors":"Hao Shu;Jicheng Li;Yu Jin;Hailin Wang","doi":"10.1109/TSP.2025.3632844","DOIUrl":"10.1109/TSP.2025.3632844","url":null,"abstract":"In recent years, the prediction of multidimensional time series data has become increasingly important due to its wide-ranging applications. Tensor-based prediction methods have gained attention for their ability to preserve the inherent structure of such data. However, existing approaches, such as tensor autoregression and tensor decomposition, often have consistently failed to provide clear assertions regarding the number of samples that can be exactly predicted. While matrix-based methods using nuclear norms address this limitation, their reliance on matrices limits accuracy and increases computational costs when handling multidimensional data. To overcome these challenges, we reformulate multidimensional time series prediction as a deterministic tensor completion problem and propose a novel theoretical framework. Specifically, we develop a deterministic tensor completion theory and introduce the <italic>Temporal Convolutional Tensor Nuclear Norm</i> (TCTNN) model. By convolving the multidimensional time series along the temporal dimension and applying the tensor nuclear norm, our approach identifies the maximum forecast horizon for exact predictions. Additionally, TCTNN achieves superior performance in prediction accuracy and computational efficiency compared to existing methods across diverse real-world datasets, including climate temperature, network flow, and traffic ride data. Our implementation is publicly available at <uri>https://github.com/HaoShu2000/TCTNN</uri>.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"4638-4653"},"PeriodicalIF":5.8,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567944","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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