Pub Date : 2024-11-13DOI: 10.1109/TSP.2024.3495552
Shih-Yu Chang;Hsiao-Chun Wu;Guannan Liu
Decorrelation of multichannel signals has played a crucial preprocessing role (in prewhitening and orthogonalization) for many signal processing applications. Classical decorrelation techniques can only be applied for signal vectors. Nonetheless, many emerging big-data and sensor-network applications involve signal tensors (signal samples required to be arranged in a tensor form of arbitrary orders). Meanwhile, the existing tensor-decorrelation methods have serious limitations. First, the correlation-tensors have to be of certain particular orders. Second, the unrealistic assumption of the specific signal-tensor form, namely the canonical polyadic (CP) form, is made. Third, the correlation-tensor has to be full-rank or an extra preprocessor based on principal component analysis is required for any non-full-rank correlation tensor. To remove the aforementioned impractical limitations, we propose a novel robust approach for high-dimensional multichannel decorrelation, which can accommodate signal tensors of arbitrary orders, forms, and ranks without any need of extra preprocessor. In this work, we introduce two new tensor-decorrelation algorithms. Our first new algorithm is designed to tackle full-rank correlation-tensors and our second new algorithm is designed to tackle non-full-rank correlation-tensors. Meanwhile, we also propose a new parallel-computing paradigm to accelerate our proposed new tensor-decorrelation algorithms. To demonstrate the applicability of our proposed new scheme, we also apply our proposed new tensor-decorrelation approach to pre-whiten the tensor signals and analyze the corresponding convergence-speed and misadjustment performances of the tensor least-mean-squares (TLMS) filter. Finally, we assess the computational- and memory-complexities of our proposed new algorithms by simulations over both artificial and real data. Simulation results show that our proposed new multichannel-decorrelation algorithms outperform the existing tensor-decorrelation methods in terms of convergence speed, eigenspread, normalized mean square error (NRMSE), and estimation accuracy.
{"title":"Robust Multichannel Decorrelation via Tensor Einstein Product","authors":"Shih-Yu Chang;Hsiao-Chun Wu;Guannan Liu","doi":"10.1109/TSP.2024.3495552","DOIUrl":"10.1109/TSP.2024.3495552","url":null,"abstract":"Decorrelation of multichannel signals has played a crucial preprocessing role (in prewhitening and orthogonalization) for many signal processing applications. Classical decorrelation techniques can only be applied for signal vectors. Nonetheless, many emerging big-data and sensor-network applications involve signal tensors (signal samples required to be arranged in a tensor form of arbitrary orders). Meanwhile, the existing tensor-decorrelation methods have serious limitations. First, the correlation-tensors have to be of certain particular orders. Second, the unrealistic assumption of the specific signal-tensor form, namely the canonical polyadic (CP) form, is made. Third, the correlation-tensor has to be full-rank or an extra preprocessor based on principal component analysis is required for any non-full-rank correlation tensor. To remove the aforementioned impractical limitations, we propose a novel robust approach for high-dimensional multichannel decorrelation, which can accommodate signal tensors of arbitrary orders, forms, and ranks without any need of extra preprocessor. In this work, we introduce two new tensor-decorrelation algorithms. Our first new algorithm is designed to tackle full-rank correlation-tensors and our second new algorithm is designed to tackle non-full-rank correlation-tensors. Meanwhile, we also propose a new parallel-computing paradigm to accelerate our proposed new tensor-decorrelation algorithms. To demonstrate the applicability of our proposed new scheme, we also apply our proposed new tensor-decorrelation approach to pre-whiten the tensor signals and analyze the corresponding convergence-speed and misadjustment performances of the tensor least-mean-squares (TLMS) filter. Finally, we assess the computational- and memory-complexities of our proposed new algorithms by simulations over both artificial and real data. Simulation results show that our proposed new multichannel-decorrelation algorithms outperform the existing tensor-decorrelation methods in terms of convergence speed, eigenspread, normalized mean square error (NRMSE), and estimation accuracy.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"275-291"},"PeriodicalIF":4.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610690","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 : 2024-11-13DOI: 10.1109/TSP.2024.3496692
Shreyas Chaudhari;Srinivasa Pranav;José M.F. Moura
Directly parameterizing and learning gradients of functions has widespread significance, with specific applications in inverse problems, generative modeling, and optimal transport. This paper introduces gradient networks ( GradNets ): novel neural network architectures that parameterize gradients of various function classes. GradNets exhibit specialized architectural constraints that ensure correspondence to gradient functions. We provide a comprehensive GradNet design framework that includes methods for transforming GradNets into monotone gradient networks ( mGradNets ), which are guaranteed to represent gradients of convex functions. Our results establish that our proposed GradNet (and mGradNet ) universally approximate the gradients of (convex) functions. Furthermore, these networks can be customized to correspond to specific spaces of potential functions, including transformed sums of (convex) ridge functions. Our analysis leads to two distinct GradNet architectures, GradNet-C and GradNet-M , and we describe the corresponding monotone versions, mGradNet-C and mGradNet-M . Our empirical results demonstrate that these architectures provide efficient parameterizations and outperform existing methods by up to 15 dB in gradient field tasks and by up to 11 dB in Hamiltonian dynamics learning tasks.
{"title":"Gradient Networks","authors":"Shreyas Chaudhari;Srinivasa Pranav;José M.F. Moura","doi":"10.1109/TSP.2024.3496692","DOIUrl":"10.1109/TSP.2024.3496692","url":null,"abstract":"Directly parameterizing and learning gradients of functions has widespread significance, with specific applications in inverse problems, generative modeling, and optimal transport. This paper introduces gradient networks (\u0000<monospace>GradNets</monospace>\u0000): novel neural network architectures that parameterize gradients of various function classes. \u0000<monospace>GradNets</monospace>\u0000 exhibit specialized architectural constraints that ensure correspondence to gradient functions. We provide a comprehensive \u0000<monospace>GradNet</monospace>\u0000 design framework that includes methods for transforming \u0000<monospace>GradNets</monospace>\u0000 into monotone gradient networks (\u0000<monospace>mGradNets</monospace>\u0000), which are guaranteed to represent gradients of convex functions. Our results establish that our proposed \u0000<monospace>GradNet</monospace>\u0000 (and \u0000<monospace>mGradNet</monospace>\u0000) universally approximate the gradients of (convex) functions. Furthermore, these networks can be customized to correspond to specific spaces of potential functions, including transformed sums of (convex) ridge functions. Our analysis leads to two distinct \u0000<monospace>GradNet</monospace>\u0000 architectures, \u0000<monospace>GradNet-C</monospace>\u0000 and \u0000<monospace>GradNet-M</monospace>\u0000, and we describe the corresponding monotone versions, \u0000<monospace>mGradNet-C</monospace>\u0000 and \u0000<monospace>mGradNet-M</monospace>\u0000. Our empirical results demonstrate that these architectures provide efficient parameterizations and outperform existing methods by up to 15 dB in gradient field tasks and by up to 11 dB in Hamiltonian dynamics learning tasks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"324-339"},"PeriodicalIF":4.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142610691","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 : 2024-11-11DOI: 10.1109/TSP.2024.3495225
Gal Morgenstern;Tirza Routtenberg
This paper investigates the recovery of a node-domain sparse graph signal from the output of a graph filter. This problem, which is often referred to as the identification of the source of a diffused sparse graph signal, is seminal in the field of graph signal processing (GSP). Sparse graph signals can be used in the modeling of a variety of real-world applications in networks, such as social, biological, and power systems, and enable various GSP tasks, such as graph signal reconstruction, blind deconvolution, and sampling. In this paper, we assume double sparsity of both the graph signal and the graph topology, as well as a low-order graph filter. We propose three algorithms to reconstruct the support set of the input sparse graph signal from the graph filter output samples, leveraging these assumptions and the generalized information criterion (GIC). First, we describe the graph multiple GIC (GM-GIC) method, which is based on partitioning the dictionary elements (graph filter matrix columns) that capture information on the signal into smaller subsets. Then, the local GICs are computed for each subset and aggregated to make a global decision. Second, inspired by the well-known branch and bound (BNB) approach, we develop the graph-based branch and bound GIC (graph-BNB-GIC), and incorporate a new tractable heuristic bound tailored to the graph and graph filter characteristics. In addition, we propose the graph-based first order correction (GFOC) method, which improves existing sparse recovery methods by iteratively examining potential improvements to the GIC cost function by replacing elements from the estimated support set with elements from their one-hop neighborhood. Simulations on stochastic block model (SBM) graphs demonstrate that the proposed sparse recovery methods outperform existing techniques in terms of support set recovery and mean-squared-error (MSE), without significant computational overhead. In addition, we investigate the application of our graph-based sparse recovery methods in blind deconvolution scenarios where the graph filter is unknown. Simulations using real-world data from brain networks and pandemic diffusion analysis further demonstrate the superiority of our approach compared to graph blind deconvolution techniques.
{"title":"Efficient Recovery of Sparse Graph Signals From Graph Filter Outputs","authors":"Gal Morgenstern;Tirza Routtenberg","doi":"10.1109/TSP.2024.3495225","DOIUrl":"10.1109/TSP.2024.3495225","url":null,"abstract":"This paper investigates the recovery of a node-domain sparse graph signal from the output of a graph filter. This problem, which is often referred to as the identification of the source of a diffused sparse graph signal, is seminal in the field of graph signal processing (GSP). Sparse graph signals can be used in the modeling of a variety of real-world applications in networks, such as social, biological, and power systems, and enable various GSP tasks, such as graph signal reconstruction, blind deconvolution, and sampling. In this paper, we assume double sparsity of both the graph signal and the graph topology, as well as a low-order graph filter. We propose three algorithms to reconstruct the support set of the input sparse graph signal from the graph filter output samples, leveraging these assumptions and the generalized information criterion (GIC). First, we describe the graph multiple GIC (GM-GIC) method, which is based on partitioning the dictionary elements (graph filter matrix columns) that capture information on the signal into smaller subsets. Then, the local GICs are computed for each subset and aggregated to make a global decision. Second, inspired by the well-known branch and bound (BNB) approach, we develop the graph-based branch and bound GIC (graph-BNB-GIC), and incorporate a new tractable heuristic bound tailored to the graph and graph filter characteristics. In addition, we propose the graph-based first order correction (GFOC) method, which improves existing sparse recovery methods by iteratively examining potential improvements to the GIC cost function by replacing elements from the estimated support set with elements from their one-hop neighborhood. Simulations on stochastic block model (SBM) graphs demonstrate that the proposed sparse recovery methods outperform existing techniques in terms of support set recovery and mean-squared-error (MSE), without significant computational overhead. In addition, we investigate the application of our graph-based sparse recovery methods in blind deconvolution scenarios where the graph filter is unknown. Simulations using real-world data from brain networks and pandemic diffusion analysis further demonstrate the superiority of our approach compared to graph blind deconvolution techniques.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5550-5566"},"PeriodicalIF":4.6,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599331","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 : 2024-11-11DOI: 10.1109/TSP.2024.3495696
Runhao Shi;Daniel P. Palomar
Strongly Adaptive meta-algorithms (SA-meta) are popular in online portfolio selection due to their resilience in adversarial environments and adaptability to market changes. However, their application is often limited by high variance in errors, stemming from calculations over small intervals with limited observations. To address this limitation, we introduce the Strongly Adaptive Optimistic Follow-the-Regularized-Leader (SAOFTRL), an advanced framework that integrates the Optimistic Follow-the-Regularized-Leader (OFTRL) strategy into SA-meta algorithms to stabilize performance. SAOFTRL is distinguished by its novel regret bound, which provides a theoretical guarantee of worst-case performance in challenging scenarios. Additionally, we reimagine SAOFTRL within a mean-variance portfolio (MVP) framework, enhanced with shrinkage estimators and adaptive rolling windows, thereby ensuring reliable average-case performance. For practical deployment, we present an efficient SAOFTRL implementation utilizing the Successive Convex Approximation (SCA) method. Empirical evaluations demonstrate SAOFTRL's superior performance and expedited convergence when compared to existing benchmarks, confirming its effectiveness and efficiency in dynamic market conditions.
{"title":"SAOFTRL: A Novel Adaptive Algorithmic Framework for Enhancing Online Portfolio Selection","authors":"Runhao Shi;Daniel P. Palomar","doi":"10.1109/TSP.2024.3495696","DOIUrl":"10.1109/TSP.2024.3495696","url":null,"abstract":"Strongly Adaptive meta-algorithms (SA-meta) are popular in online portfolio selection due to their resilience in adversarial environments and adaptability to market changes. However, their application is often limited by high variance in errors, stemming from calculations over small intervals with limited observations. To address this limitation, we introduce the Strongly Adaptive Optimistic Follow-the-Regularized-Leader (SAOFTRL), an advanced framework that integrates the Optimistic Follow-the-Regularized-Leader (OFTRL) strategy into SA-meta algorithms to stabilize performance. SAOFTRL is distinguished by its novel regret bound, which provides a theoretical guarantee of worst-case performance in challenging scenarios. Additionally, we reimagine SAOFTRL within a mean-variance portfolio (MVP) framework, enhanced with shrinkage estimators and adaptive rolling windows, thereby ensuring reliable average-case performance. For practical deployment, we present an efficient SAOFTRL implementation utilizing the Successive Convex Approximation (SCA) method. Empirical evaluations demonstrate SAOFTRL's superior performance and expedited convergence when compared to existing benchmarks, confirming its effectiveness and efficiency in dynamic market conditions.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5291-5305"},"PeriodicalIF":4.6,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599330","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 : 2024-11-07DOI: 10.1109/tsp.2024.3493603
Jakob Möderl, Erik Leitinger, Franz Pernkopf, Klaus Witrisal
{"title":"Variational Inference of Structured Line Spectra Exploiting Group-Sparsity","authors":"Jakob Möderl, Erik Leitinger, Franz Pernkopf, Klaus Witrisal","doi":"10.1109/tsp.2024.3493603","DOIUrl":"https://doi.org/10.1109/tsp.2024.3493603","url":null,"abstract":"","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"6 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597757","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 : 2024-11-06DOI: 10.1109/TSP.2024.3492692
Tingting Zhang;Sergiy A. Vorobyov;Feng Xu
We present a novel slow-time transmit beamspace (TB) multiple-input multiple-output (MIMO) technique for L-shaped array radar with uniform linear subarrays to estimate target parameters including 2-dimensional (2-D) directions of arrival (DOA) and unambiguous velocity. Doppler division multiple access (DDMA) approach, as a type of slow-time waveform achieving waveform orthogonality across multiple pulses within a coherent processing interval, disperses the transmit energy over the entire spatial region, suffering from beam-shape loss. Moreover, Doppler spectrum division, which is necessary for transmit channel separation prior to parameter estimation, leads to the loss of crucial information for velocity disambiguation. To optimize transmit energy distribution, slow-time TB technique is proposed to focus the energy within a desired spatial region. Unlike DDMA approach, slow-time TB technique divides the entire Doppler spectrum into more subbands than the number of transmit antenna elements to narrow down the beam mainlobe intervals between adjacent beams formed by DDMA modulation vectors. As a result, more beams are incorporated into the region of interest, and slow-time TB radar can direct transmit energy to the region of interest by properly selecting the DDMA modulation vectors whose beams are directed there. To resolve velocity ambiguity, tensor signal modeling, by storing measurements in a tensor without Doppler spectrum division, is used. Parameter estimation is then addressed using canonical polyadic decomposition (CPD), and the performance of slow-time TB L-shaped MIMO radar is shown to be improved as compared to DDMA MIMO techniques. Simulations are conducted to validate the proposed method.
我们为带有均匀线性子阵列的 L 形阵列雷达提出了一种新型慢时发射波束空间（TB）多输入多输出（MIMO）技术，用于估计目标参数，包括二维（2-D）到达方向（DOA）和明确的速度。多普勒频分多址（DDMA）方法作为一种慢时波形，可在一个相干处理间隔内通过多个脉冲实现波形正交，但会将发射能量分散到整个空间区域，从而造成波束形状损失。此外，多普勒频谱划分对于参数估计前的发射信道分离十分必要，但却会导致速度消歧的关键信息丢失。为了优化发射能量分布，提出了慢速 TB 技术，将能量集中在所需的空间区域内。与 DDMA 方法不同，慢时 TB 技术将整个多普勒频谱划分为比发射天线元件数量更多的子带，以缩小由 DDMA 调制矢量形成的相邻波束之间的波束主间隔。因此，更多波束被纳入感兴趣区域，慢时 TB 雷达可通过适当选择波束指向感兴趣区域的 DDMA 调制矢量，将发射能量导向感兴趣区域。为了解决速度模糊性问题，采用了张量信号建模，将测量数据存储在张量中，而不进行多普勒频谱划分。然后使用规范多义分解（CPD）进行参数估计，结果表明，与 DDMA MIMO 技术相比，慢时 TB L 型 MIMO 雷达的性能有所提高。仿真验证了所提出的方法。
{"title":"Transmit Energy Focusing for Parameter Estimation in Slow-Time Transmit Beamspace L-Shaped MIMO Radar","authors":"Tingting Zhang;Sergiy A. Vorobyov;Feng Xu","doi":"10.1109/TSP.2024.3492692","DOIUrl":"10.1109/TSP.2024.3492692","url":null,"abstract":"We present a novel slow-time transmit beamspace (TB) multiple-input multiple-output (MIMO) technique for L-shaped array radar with uniform linear subarrays to estimate target parameters including 2-dimensional (2-D) directions of arrival (DOA) and unambiguous velocity. Doppler division multiple access (DDMA) approach, as a type of slow-time waveform achieving waveform orthogonality across multiple pulses within a coherent processing interval, disperses the transmit energy over the entire spatial region, suffering from beam-shape loss. Moreover, Doppler spectrum division, which is necessary for transmit channel separation prior to parameter estimation, leads to the loss of crucial information for velocity disambiguation. To optimize transmit energy distribution, slow-time TB technique is proposed to focus the energy within a desired spatial region. Unlike DDMA approach, slow-time TB technique divides the entire Doppler spectrum into more subbands than the number of transmit antenna elements to narrow down the beam mainlobe intervals between adjacent beams formed by DDMA modulation vectors. As a result, more beams are incorporated into the region of interest, and slow-time TB radar can direct transmit energy to the region of interest by properly selecting the DDMA modulation vectors whose beams are directed there. To resolve velocity ambiguity, tensor signal modeling, by storing measurements in a tensor without Doppler spectrum division, is used. Parameter estimation is then addressed using canonical polyadic decomposition (CPD), and the performance of slow-time TB L-shaped MIMO radar is shown to be improved as compared to DDMA MIMO techniques. Simulations are conducted to validate the proposed method.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5228-5243"},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10746379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we address the problem of polarimetric detection of a target with energy spillover in Gaussian environment. We adopt full polarized channels and use diverse criteria to design detectors, resulting in five new adaptive detectors. Performance evaluation reveals that the derived detectors gain higher probabilities of detection (PD) and better localization abilities, compared to the detectors which only use single-channel or dual-channel polarized data. A knowledge-aided detector is also proposed to give a further boost in detection and localization performances, especially under small number of secondary data. Besides, all the derived detectors guarantee the bounded constant false alarm rate (CFAR) properties. Experiments with real data from IPIX radar validate the superior performance and the practicability of the proposed detectors.
{"title":"Polarization Diversity Detection and Localization of a Target With Energy Spillover","authors":"Naixin Kang;Weijian Liu;Jun Liu;Chengpeng Hao;Xiaotao Huang;Zheran Shang","doi":"10.1109/TSP.2024.3490844","DOIUrl":"10.1109/TSP.2024.3490844","url":null,"abstract":"In this paper, we address the problem of polarimetric detection of a target with energy spillover in Gaussian environment. We adopt full polarized channels and use diverse criteria to design detectors, resulting in five new adaptive detectors. Performance evaluation reveals that the derived detectors gain higher probabilities of detection (PD) and better localization abilities, compared to the detectors which only use single-channel or dual-channel polarized data. A knowledge-aided detector is also proposed to give a further boost in detection and localization performances, especially under small number of secondary data. Besides, all the derived detectors guarantee the bounded constant false alarm rate (CFAR) properties. Experiments with real data from IPIX radar validate the superior performance and the practicability of the proposed detectors.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"401-417"},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594836","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 : 2024-11-05DOI: 10.1109/TSP.2024.3491899
Yumeng Zhang;Sundar Aditya;Bruno Clerckx
Orthogonal frequency division multiplexing (OFDM) has been widely adopted in dual-function radar-communication (DFRC) systems. However, with random communication symbols (CS) embedded in the DFRC waveform, the transmit signal has a random ambiguity function that affects the radar's delay-Doppler estimation performance, which has not been well explored. This paper addresses this gap by first characterizing the outlier probability (OP) – the probability of incorrectly estimating a target's (on-grid) delay-Doppler bin – in OFDM DFRC for any given CS realization. This subsequently motivates the OFDM DFRC waveform design problem of minimizing the OP w.r.t the CS probability distribution (i.e., the input distribution ). Conditioned on the CSs, the OP only depends on the CS magnitudes. Hence, we consider the following two schemes for the above optimization: CSs with (1) constant magnitude input distribution (phase shift keying), and (2) variable magnitude input distribution (Gaussian). For (1), minimizing the OP reduces to the familiar power allocation design across OFDM's subcarriers and symbols, with uniform power allocation across OFDM subcarriers and a windowed power allocation across OFDM symbols being near-optimal. For (2), the mean and variance of the Gaussian distribution at each subcarrier is optimized, with an additional communication constraint to avoid the zero-variance solution where no CSs are carried. We observe that subcarriers with strong communication channels feature a large variance (favour communications) while the others are characterized by a large mean (favour radar). However, the overall power allocation (i.e., the sum of the squared mean and variance) across the OFDM subcarriers and symbols is similar to (1). Simulations for (2) show that while random CS magnitudes benefit communications, they degrade radar performance, but this can be mitigated using our optimized input distribution.
{"title":"Input Distribution Optimization in OFDM Dual-Function Radar-Communication Systems","authors":"Yumeng Zhang;Sundar Aditya;Bruno Clerckx","doi":"10.1109/TSP.2024.3491899","DOIUrl":"10.1109/TSP.2024.3491899","url":null,"abstract":"Orthogonal frequency division multiplexing (OFDM) has been widely adopted in dual-function radar-communication (DFRC) systems. However, with random communication symbols (CS) embedded in the DFRC waveform, the transmit signal has a random ambiguity function that affects the radar's delay-Doppler estimation performance, which has not been well explored. This paper addresses this gap by first characterizing the outlier probability (OP) – the probability of incorrectly estimating a target's (on-grid) delay-Doppler bin – in OFDM DFRC for any given CS realization. This subsequently motivates the OFDM DFRC waveform design problem of minimizing the OP w.r.t the CS probability distribution (i.e., the \u0000<italic>input distribution</i>\u0000). Conditioned on the CSs, the OP only depends on the CS magnitudes. Hence, we consider the following two schemes for the above optimization: CSs with (1) constant magnitude input distribution (phase shift keying), and (2) variable magnitude input distribution (Gaussian). For (1), minimizing the OP reduces to the familiar power allocation design across OFDM's subcarriers and symbols, with uniform power allocation across OFDM subcarriers and a \u0000<italic>windowed</i>\u0000 power allocation across OFDM symbols being near-optimal. For (2), the mean and variance of the Gaussian distribution at each subcarrier is optimized, with an additional communication constraint to avoid the zero-variance solution where no CSs are carried. We observe that subcarriers with strong communication channels feature a large variance (favour communications) while the others are characterized by a large mean (favour radar). However, the overall power allocation (i.e., the sum of the squared mean and variance) across the OFDM subcarriers and symbols is similar to (1). Simulations for (2) show that while random CS magnitudes benefit communications, they degrade radar performance, but this can be mitigated using our optimized input distribution.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5258-5273"},"PeriodicalIF":4.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142588772","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 : 2024-11-05DOI: 10.1109/TSP.2024.3491993
Seyed Mohammad Azimi-Abarghouyi;Lav R. Varshney
This paper introduces a federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme. Without relying on channel state information at devices, this scheme employs lattice codes to both quantize model parameters and exploit interference from the devices. We propose a novel receiver structure at the server, designed to reliably decode an integer combination of the quantized model parameters as a lattice point for the purpose of aggregation. We present a mathematical approach to derive a convergence bound for the proposed scheme and offer design remarks. In this context, we suggest an aggregation metric and a corresponding algorithm to determine effective integer coefficients for the aggregation in each communication round. Our results illustrate that, regardless of channel dynamics and data heterogeneity, our scheme consistently delivers superior learning accuracy across various parameters and markedly surpasses other over-the-air methodologies.
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Pub Date : 2024-11-05DOI: 10.1109/TSP.2024.3492165
Ege C. Kaya;Abolfazl Hashemi
In this work, we approach the problem of multi-task submodular optimization with the perspective of local distributional robustness, within the neighborhood of a reference distribution which assigns an importance score to each task. We initially propose to introduce a regularization term which makes use of the relative entropy to the standard multi-task objective. We then demonstrate through duality that this novel formulation itself is equivalent to the maximization of a monotone increasing function composed with a submodular function, which may be efficiently carried out through standard greedy selection methods. This approach bridges the existing gap in the optimization of performance-robustness trade-offs in multi-task subset selection. To numerically validate our theoretical results, we test the proposed method in two different settings, one on the selection of satellites in low Earth orbit constellations in the context of a sensor selection problem involving weak-submodular functions, and the other on an image summarization task using neural networks involving submodular functions. Our method is compared with two other algorithms focused on optimizing the performance of the worst-case task, and on directly optimizing the performance on the reference distribution itself. We conclude that our novel formulation produces a solution that is locally distributional robust, and computationally inexpensive.
{"title":"Localized Distributional Robustness in Submodular Multi-Task Subset Selection","authors":"Ege C. Kaya;Abolfazl Hashemi","doi":"10.1109/TSP.2024.3492165","DOIUrl":"10.1109/TSP.2024.3492165","url":null,"abstract":"In this work, we approach the problem of multi-task submodular optimization with the perspective of local distributional robustness, within the neighborhood of a reference distribution which assigns an importance score to each task. We initially propose to introduce a regularization term which makes use of the relative entropy to the standard multi-task objective. We then demonstrate through duality that this novel formulation itself is equivalent to the maximization of a monotone increasing function composed with a submodular function, which may be efficiently carried out through standard greedy selection methods. This approach bridges the existing gap in the optimization of performance-robustness trade-offs in multi-task subset selection. To numerically validate our theoretical results, we test the proposed method in two different settings, one on the selection of satellites in low Earth orbit constellations in the context of a sensor selection problem involving weak-submodular functions, and the other on an image summarization task using neural networks involving submodular functions. Our method is compared with two other algorithms focused on optimizing the performance of the worst-case task, and on directly optimizing the performance on the reference distribution itself. We conclude that our novel formulation produces a solution that is locally distributional robust, and computationally inexpensive.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5338-5352"},"PeriodicalIF":4.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142588775","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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