Pub Date : 2024-09-25DOI: 10.1109/TSP.2024.3468435
Quanzhou Yu;Yongqing Wang;Yuyao Shen
Distributed cooperative localization (CL) possesses the merits of high accuracy, robustness, and availability, and has garnered extensive attention in recent years. Due to the complex signal propagation environment, measurements often include errors from various unknown factors, leading to a mismatch between the nominal and actual measurement models, which reduces estimation accuracy. To tackle this problem, this paper proposes a robust distributed CL algorithm. First, we establish a unified measurement model incorporating latent variables capable of characterizing nonideal errors in the absence of additional prior environmental information. The latent variables are modeled using Gaussian-Wishart conjugate prior distribution with hyperparameters. Next, we decompose the robust CL problem into the alternate estimation of the variational posterior for agent positions and latent variables. By constructing the probabilistic graphical model, the estimation can be implemented in a distributed manner via the message passing framework. Closed-form solutions are derived for updating the variational posteriors of agent positions and latent variables, ensuring all parameters can be computed algebraically. Additionally, we analyze the algorithm's performance, computational complexity, and communication overhead. Simulation and experimental results show that the proposed algorithm exhibits superior estimation accuracy and robustness compared to existing methods.
{"title":"Robust Distributed Cooperative Localization in Wireless Sensor Networks With a Mismatched Measurement Model","authors":"Quanzhou Yu;Yongqing Wang;Yuyao Shen","doi":"10.1109/TSP.2024.3468435","DOIUrl":"10.1109/TSP.2024.3468435","url":null,"abstract":"Distributed cooperative localization (CL) possesses the merits of high accuracy, robustness, and availability, and has garnered extensive attention in recent years. Due to the complex signal propagation environment, measurements often include errors from various unknown factors, leading to a mismatch between the nominal and actual measurement models, which reduces estimation accuracy. To tackle this problem, this paper proposes a robust distributed CL algorithm. First, we establish a unified measurement model incorporating latent variables capable of characterizing nonideal errors in the absence of additional prior environmental information. The latent variables are modeled using Gaussian-Wishart conjugate prior distribution with hyperparameters. Next, we decompose the robust CL problem into the alternate estimation of the variational posterior for agent positions and latent variables. By constructing the probabilistic graphical model, the estimation can be implemented in a distributed manner via the message passing framework. Closed-form solutions are derived for updating the variational posteriors of agent positions and latent variables, ensuring all parameters can be computed algebraically. Additionally, we analyze the algorithm's performance, computational complexity, and communication overhead. Simulation and experimental results show that the proposed algorithm exhibits superior estimation accuracy and robustness compared to existing methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4525-4540"},"PeriodicalIF":4.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142321698","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 : 2024-09-25DOI: 10.1109/TSP.2024.3468467
Geng Wang;Shenghong Li;Peng Cheng;Branka Vucetic;Yonghui Li
Accurate indoor localization remains a significant challenge, primarily due to multipath and non-line-of-sight (NLoS) propagation conditions in complex indoor environments. Traditional localization methods often rely on oversimplified assumptions or require prior knowledge of channel or ranging error statistics. Unfortunately, these approaches overlook the environment/location-dependent nature of the ranging error, e.g., highly dynamic and unpredictable, resulting in sub-optimal performances in real-world settings. To address these challenges, we introduce a novel Bayesian tracking framework that simultaneously tracks the statistics of ranging errors and target's location for fine-grained ranging error mitigation, without the need for prior knowledge of the channel or environment. The proposed method characterizes the distribution of ranging error using mixture distributions with dynamically updated parameters. A hidden Markov model (HMM) is employed to track the sight condition (i.e. LoS or NLoS) of the propagation channel and adjust the parameters of the ranging error model online. Our proposed framework focuses on 802.11 range-based localization systems and aims to deliver general-purpose localization services where sub-meter level accuracy is sufficient. Experimental evaluations conducted across two real-world indoor scenarios demonstrate that the proposed method significantly improves localization accuracy to 1 meter in challenging multipath and NLoS environments, outperforming existing techniques while maintaining similar computation complexity.
主要由于复杂室内环境中的多径和非视距（NLoS）传播条件，精确的室内定位仍然是一项重大挑战。传统的定位方法通常依赖于过于简化的假设，或者需要事先了解信道或测距误差统计。遗憾的是，这些方法忽视了测距误差与环境/位置相关的特性，例如高度动态和不可预测，从而导致在实际环境中无法达到最佳性能。为了应对这些挑战，我们引入了一种新颖的贝叶斯跟踪框架，该框架可同时跟踪测距误差统计和目标位置，以减轻细粒度测距误差，而无需事先了解信道或环境。所提出的方法使用动态更新参数的混合分布来描述测距误差的分布特征。采用隐马尔可夫模型（HMM）来跟踪传播信道的视线条件（即 LoS 或 NLoS），并在线调整测距误差模型的参数。我们提出的框架侧重于基于 802.11 范围的定位系统，旨在提供通用的定位服务，其中亚米级精度就已足够。在两个真实的室内场景中进行的实验评估表明，所提出的方法在具有挑战性的多径和 NLoS 环境中显著提高了 1 米级的定位精度，在保持类似计算复杂度的情况下优于现有技术。
{"title":"ToF-Based NLoS Indoor Tracking With Adaptive Ranging Error Mitigation","authors":"Geng Wang;Shenghong Li;Peng Cheng;Branka Vucetic;Yonghui Li","doi":"10.1109/TSP.2024.3468467","DOIUrl":"10.1109/TSP.2024.3468467","url":null,"abstract":"Accurate indoor localization remains a significant challenge, primarily due to multipath and non-line-of-sight (NLoS) propagation conditions in complex indoor environments. Traditional localization methods often rely on oversimplified assumptions or require prior knowledge of channel or ranging error statistics. Unfortunately, these approaches overlook the environment/location-dependent nature of the ranging error, e.g., highly dynamic and unpredictable, resulting in sub-optimal performances in real-world settings. To address these challenges, we introduce a novel Bayesian tracking framework that simultaneously tracks the statistics of ranging errors and target's location for fine-grained ranging error mitigation, without the need for prior knowledge of the channel or environment. The proposed method characterizes the distribution of ranging error using mixture distributions with dynamically updated parameters. A hidden Markov model (HMM) is employed to track the sight condition (i.e. LoS or NLoS) of the propagation channel and adjust the parameters of the ranging error model online. Our proposed framework focuses on 802.11 range-based localization systems and aims to deliver general-purpose localization services where sub-meter level accuracy is sufficient. Experimental evaluations conducted across two real-world indoor scenarios demonstrate that the proposed method significantly improves localization accuracy to 1 meter in challenging multipath and NLoS environments, outperforming existing techniques while maintaining similar computation complexity.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4855-4870"},"PeriodicalIF":4.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142321699","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 : 2024-09-23DOI: 10.1109/TSP.2024.3464753
Jean Pinsolle;Olivier Goudet;Cyrille Enderli;Sylvain Lamprier;Jin-Kao Hao
In this paper, we propose a new deinterleaving method for mixtures of discrete renewal Markov chains. This method relies on the maximization of a penalized likelihood score. It exploits all available information about both the sequence of the different symbols and their arrival times. A theoretical analysis is carried out to prove that minimizing this score allows to recover the true partition of symbols in the large sample limit, under mild conditions on the component processes. This theoretical analysis is then validated by experiments on synthetic data. Finally, the method is applied to deinterleave pulse trains received from different emitters in a RESM (Radar Electronic Support Measurements) context and we show that the proposed method competes favorably with state-of-the-art methods on simulated warfare datasets.
{"title":"Deinterleaving of Discrete Renewal Process Mixtures With Application to Electronic Support Measures","authors":"Jean Pinsolle;Olivier Goudet;Cyrille Enderli;Sylvain Lamprier;Jin-Kao Hao","doi":"10.1109/TSP.2024.3464753","DOIUrl":"10.1109/TSP.2024.3464753","url":null,"abstract":"In this paper, we propose a new deinterleaving method for mixtures of discrete renewal Markov chains. This method relies on the maximization of a penalized likelihood score. It exploits all available information about both the sequence of the different symbols and their arrival times. A theoretical analysis is carried out to prove that minimizing this score allows to recover the true partition of symbols in the large sample limit, under mild conditions on the component processes. This theoretical analysis is then validated by experiments on synthetic data. Finally, the method is applied to deinterleave pulse trains received from different emitters in a RESM (Radar Electronic Support Measurements) context and we show that the proposed method competes favorably with state-of-the-art methods on simulated warfare datasets.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4983-4991"},"PeriodicalIF":4.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313776","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 : 2024-09-23DOI: 10.1109/TSP.2024.3465499
Zhen Du;Fan Liu;Yifeng Xiong;Tony Xiao Han;Yonina C. Eldar;Shi Jin
Integrated sensing and communications is regarded as a key enabling technology in the sixth generation networks, where a unified waveform, such as orthogonal frequency division multiplexing (OFDM) signal, is adopted to facilitate both sensing and communications (S&C). However, the random communication data embedded in the OFDM signal results in severe variability in the sidelobes of its ambiguity function (AF), which leads to missed detection of weak targets and false detection of ghost targets, thereby impairing the sensing performance. Therefore, balancing between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, we characterize the random AF of OFDM communication signals, and demonstrate that the AF variance is determined by the fourth-moment of the constellation amplitudes. Subsequently, we propose an optimal probabilistic constellation shaping (PCS) approach by maximizing the achievable information rate (AIR) under the fourth-moment, power and probability constraints, where the optimal input distribution may be numerically specified through a modified Blahut-Arimoto algorithm. To reduce the computational overheads, we further propose a heuristic PCS approach by actively controlling the value of the fourth-moment, without involving the communication metric in the optimization model, despite that the AIR is passively scaled with the variation of the input distribution. Numerical results show that both approaches strike a scalable performance tradeoff between S&C, where the superiority of the PCS-enabled constellations over conventional uniform constellations is also verified. Notably, the heuristic approach achieves very close performance to the optimal counterpart, at a much lower computational complexity.
综合传感与通信被视为第六代网络的一项关键使能技术，它采用统一的波形，如正交频分复用（OFDM）信号，以促进传感与通信（S&C）。然而，OFDM 信号中嵌入的随机通信数据会导致其模糊函数（AF）的边沿发生严重变化，从而导致漏检弱目标和误检幽灵目标，进而影响传感性能。因此，如何在保持通信能力（即随机性）和提高传感性能之间取得平衡仍然是一项具有挑战性的任务。为了解决这个问题，我们描述了 OFDM 通信信号的随机 AF 特性，并证明 AF 方差由星座振幅的四分频决定。随后，我们提出了一种最优概率星座整形（PCS）方法，即在第四时刻、功率和概率约束条件下最大化可实现信息率（AIR），其中最优输入分布可通过改进的 Blahut-Arimoto 算法进行数值指定。为了减少计算开销，我们进一步提出了一种启发式 PCS 方法，即通过主动控制第四时刻的值，而不将通信指标纳入优化模型，尽管 AIR 会随着输入分布的变化而被动缩放。数值结果表明，这两种方法都在 S&C 之间实现了可扩展的性能权衡，同时也验证了 PCS 星群优于传统的均匀星群。值得注意的是，启发式方法以更低的计算复杂度实现了与最优方法非常接近的性能。
{"title":"Reshaping the ISAC Tradeoff Under OFDM Signaling: A Probabilistic Constellation Shaping Approach","authors":"Zhen Du;Fan Liu;Yifeng Xiong;Tony Xiao Han;Yonina C. Eldar;Shi Jin","doi":"10.1109/TSP.2024.3465499","DOIUrl":"10.1109/TSP.2024.3465499","url":null,"abstract":"Integrated sensing and communications is regarded as a key enabling technology in the sixth generation networks, where a unified waveform, such as orthogonal frequency division multiplexing (OFDM) signal, is adopted to facilitate both sensing and communications (S&C). However, the random communication data embedded in the OFDM signal results in severe variability in the sidelobes of its ambiguity function (AF), which leads to missed detection of weak targets and false detection of ghost targets, thereby impairing the sensing performance. Therefore, balancing between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, we characterize the random AF of OFDM communication signals, and demonstrate that the AF variance is determined by the fourth-moment of the constellation amplitudes. Subsequently, we propose an optimal probabilistic constellation shaping (PCS) approach by maximizing the achievable information rate (AIR) under the fourth-moment, power and probability constraints, where the optimal input distribution may be numerically specified through a modified Blahut-Arimoto algorithm. To reduce the computational overheads, we further propose a heuristic PCS approach by actively controlling the value of the fourth-moment, without involving the communication metric in the optimization model, despite that the AIR is passively scaled with the variation of the input distribution. Numerical results show that both approaches strike a scalable performance tradeoff between S&C, where the superiority of the PCS-enabled constellations over conventional uniform constellations is also verified. Notably, the heuristic approach achieves very close performance to the optimal counterpart, at a much lower computational complexity.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4782-4797"},"PeriodicalIF":4.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10685511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313492","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}
Pub Date : 2024-09-23DOI: 10.1109/TSP.2024.3465842
Gilles Monnoyer;Thomas Feuillen;Luc Vandendorpe;Laurent Jacques
In radars, sonars, or for sound source localization, sensor networks enable the estimation of parameters that cannot be unambiguously recovered by a single sensor. The estimation algorithms designed for this context are commonly divided into two categories: the two-step methods, separately estimating intermediate parameters in each sensor before combining them; and the single-step methods jointly processing all the received signals. This paper provides a general framework, coined Grid Hopping (GH), unifying existing techniques to accelerate the single-step methods, known to provide robust results with a higher computational time. GH exploits interpolation to approximate evaluations of correlation functions from the coarser grid used in two-step methods onto the finer grid required for single-step methods, hence “hopping” from one grid to the other. The contribution of this paper is two-fold. We first formulate GH, showing its particularization to existing acceleration techniques used in multiple applications. Second, we derive a novel theoretical bound characterizing the performance loss caused by GH in simplified scenarios. We finally provide Monte-Carlo simulations demonstrating how GH preserves the advantages of both the single-step and two-step approaches and compare its performance when used with multiple interpolation techniques.
{"title":"Grid Hopping in Sensor Networks: Acceleration Strategies for Single-Step Estimation Algorithms","authors":"Gilles Monnoyer;Thomas Feuillen;Luc Vandendorpe;Laurent Jacques","doi":"10.1109/TSP.2024.3465842","DOIUrl":"10.1109/TSP.2024.3465842","url":null,"abstract":"In radars, sonars, or for sound source localization, sensor networks enable the estimation of parameters that cannot be unambiguously recovered by a single sensor. The estimation algorithms designed for this context are commonly divided into two categories: the two-step methods, separately estimating intermediate parameters in each sensor before combining them; and the single-step methods jointly processing all the received signals. This paper provides a general framework, coined Grid Hopping (GH), unifying existing techniques to accelerate the single-step methods, known to provide robust results with a higher computational time. GH exploits interpolation to approximate evaluations of correlation functions from the coarser grid used in two-step methods onto the finer grid required for single-step methods, hence “hopping” from one grid to the other. The contribution of this paper is two-fold. We first formulate GH, showing its particularization to existing acceleration techniques used in multiple applications. Second, we derive a novel theoretical bound characterizing the performance loss caused by GH in simplified scenarios. We finally provide Monte-Carlo simulations demonstrating how GH preserves the advantages of both the single-step and two-step approaches and compare its performance when used with multiple interpolation techniques.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4463-4478"},"PeriodicalIF":4.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313778","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 : 2024-09-20DOI: 10.1109/TSP.2024.3454986
Richeng Jin;Xiaofan He;Caijun Zhong;Zhaoyang Zhang;Tony Q. S. Quek;Huaiyu Dai
Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks. To alleviate the concern, various gradient compression methods have been proposed, and sign-based algorithms are of surging interest. However, sign SGD fails to converge in the presence of data heterogeneity, which is commonly observed in the emerging federated learning (FL) paradigm. Error feedback has been proposed to address the non-convergence issue. Nonetheless, it requires the workers to locally keep track of the compression errors, which renders it not suitable for FL since the workers may not participate in the training throughout the learning process. In this paper, we propose a magnitude-driven sparsification scheme, which addresses the non-convergence issue of sign SGD while further improving communication efficiency. Moreover, the local update and the error feedback schemes are further incorporated to improve the learning performance (i.e., test accuracy and communication efficiency), and the convergence of the proposed method is established. The effectiveness of the proposed scheme is validated through extensive experiments on Fashion-MNIST, CIFAR-10, CIFAR-100, Tiny-ImageNet, and Mini-ImageNet datasets.
{"title":"Magnitude Matters: Fixing signSGD Through Magnitude-Aware Sparsification and Error Feedback in the Presence of Data Heterogeneity","authors":"Richeng Jin;Xiaofan He;Caijun Zhong;Zhaoyang Zhang;Tony Q. S. Quek;Huaiyu Dai","doi":"10.1109/TSP.2024.3454986","DOIUrl":"10.1109/TSP.2024.3454986","url":null,"abstract":"Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks. To alleviate the concern, various gradient compression methods have been proposed, and sign-based algorithms are of surging interest. However, \u0000<sc>sign</small>\u0000SGD fails to converge in the presence of data heterogeneity, which is commonly observed in the emerging federated learning (FL) paradigm. Error feedback has been proposed to address the non-convergence issue. Nonetheless, it requires the workers to locally keep track of the compression errors, which renders it not suitable for FL since the workers may not participate in the training throughout the learning process. In this paper, we propose a magnitude-driven sparsification scheme, which addresses the non-convergence issue of \u0000<sc>sign</small>\u0000SGD while further improving communication efficiency. Moreover, the local update and the error feedback schemes are further incorporated to improve the learning performance (i.e., test accuracy and communication efficiency), and the convergence of the proposed method is established. The effectiveness of the proposed scheme is validated through extensive experiments on Fashion-MNIST, CIFAR-10, CIFAR-100, Tiny-ImageNet, and Mini-ImageNet datasets.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5140-5155"},"PeriodicalIF":4.6,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275427","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 : 2024-09-18DOI: 10.1109/TSP.2024.3463210
Jiechen Chen;Sangwoo Park;Petar Popovski;H. Vincent Poor;Osvaldo Simeone
Neuromorphic computing leverages the sparsity of temporal data to reduce processing energy by activating a small subset of neurons and synapses at each time step. When deployed for split computing in edge-based systems, remote neuromorphic processing units (NPUs) can reduce the communication power budget by communicating asynchronously using sparse impulse radio (IR) waveforms. This way, the input signal sparsity translates directly into energy savings both in terms of computation and communication. However, with IR transmission, the main contributor to the overall energy consumption remains the power required to maintain the main radio on. This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making. To address this problem, as a second contribution, this work proposes a novel methodology that leverages the use of a digital twin (DT), i.e., a simulator, of the physical system, coupled with a sequential statistical testing approach known as Learn Then Test (LTT) to provide theoretical reliability guarantees. The proposed DT-LTT methodology is broadly applicable to other design problems, and is showcased here for neuromorphic communications. Experimental results validate the design and the analysis, confirming the theoretical reliability guarantees and illustrating trade-offs among reliability, energy consumption, and informativeness of the decisions.
{"title":"Neuromorphic Split Computing With Wake-Up Radios: Architecture and Design via Digital Twinning","authors":"Jiechen Chen;Sangwoo Park;Petar Popovski;H. Vincent Poor;Osvaldo Simeone","doi":"10.1109/TSP.2024.3463210","DOIUrl":"10.1109/TSP.2024.3463210","url":null,"abstract":"Neuromorphic computing leverages the sparsity of temporal data to reduce processing energy by activating a small subset of neurons and synapses at each time step. When deployed for split computing in edge-based systems, remote neuromorphic processing units (NPUs) can reduce the communication power budget by communicating asynchronously using sparse impulse radio (IR) waveforms. This way, the input signal sparsity translates directly into energy savings both in terms of computation and communication. However, with IR transmission, the main contributor to the overall energy consumption remains the power required to maintain the main radio on. This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making. To address this problem, as a second contribution, this work proposes a novel methodology that leverages the use of a digital twin (DT), i.e., a simulator, of the physical system, coupled with a sequential statistical testing approach known as Learn Then Test (LTT) to provide theoretical reliability guarantees. The proposed DT-LTT methodology is broadly applicable to other design problems, and is showcased here for neuromorphic communications. Experimental results validate the design and the analysis, confirming the theoretical reliability guarantees and illustrating trade-offs among reliability, energy consumption, and informativeness of the decisions.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4635-4650"},"PeriodicalIF":4.6,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245798","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 : 2024-09-17DOI: 10.1109/TSP.2024.3461720
Pranav Kulkarni;P. P. Vaidyanathan
In recent years, following the development of nested arrays and coprime arrays, several improved array constructions have been proposed to identify <inline-formula><tex-math>$mathcal{O}(N^{2})$</tex-math></inline-formula> directions with <inline-formula><tex-math>$N$</tex-math></inline-formula> sensors and to reduce the impact of mutual coupling on the direction of arrival (DOA) estimation. However, having <inline-formula><tex-math>$mathcal{O}(N^{2})$</tex-math></inline-formula> degrees of freedom may not be of interest, especially for large <inline-formula><tex-math>$N$</tex-math></inline-formula> . Also, a large aperture of such arrays may not be suitable when limited space is available to place the sensors. This paper presents two types of sparse array designs that can effectively handle high mutual coupling by ensuring that the coarray weights satisfy either <inline-formula><tex-math>$w(1)=0$</tex-math></inline-formula> or <inline-formula><tex-math>$w(1)=w(2)=0$</tex-math></inline-formula> , where <inline-formula><tex-math>$w(l)$</tex-math></inline-formula> is the number of occurrences of the difference <inline-formula><tex-math>$l$</tex-math></inline-formula> in the set <inline-formula><tex-math>${n_{i}-n_{j}}_{i,j=1}^{N}$</tex-math></inline-formula> , and <inline-formula><tex-math>$n_{i}$</tex-math></inline-formula> are sensors locations. In addition, several other coarray weights are small constants that do not increase with the number of sensors <inline-formula><tex-math>$N$</tex-math></inline-formula> . The arrays of the first type have an aperture of <inline-formula><tex-math>$mathcal{O}(N)$</tex-math></inline-formula> length, making them suitable when the available aperture is restricted and the number of DOAs is also <inline-formula><tex-math>$mathcal{O}(N)$</tex-math></inline-formula> . These arrays are constructed by appropriately dilating a uniform linear array (ULA) and augmenting a few additional sensors. Despite having an aperture of <inline-formula><tex-math>$mathcal{O}(N)$</tex-math></inline-formula> length, these arrays can still identify more than <inline-formula><tex-math>$N$</tex-math></inline-formula> DOAs. The arrays of the second type have <inline-formula><tex-math>$mathcal{O}(N^{2})$</tex-math></inline-formula> degrees of freedom and are suitable when the aperture is not restricted. These arrays are constructed by appropriately dilating a nested array and augmenting it with several additional sensors. We compare the proposed arrays with those in the literature by analyzing their coarray properties and conducting several Monte-Carlo simulations. Unlike ULA and nested array, any sensor pair in the proposed arrays has a spacing of at least 2 units, because of the coarray hole at lag 1. In the presence of high mutual coupling, the proposed arrays can estimate DOAs with significantly smaller errors when compared to other arrays because of the reduction of coarray weight at critical small-valued la
{"title":"Weight-Constrained Sparse Arrays For Direction of Arrival Estimation Under High Mutual Coupling","authors":"Pranav Kulkarni;P. P. Vaidyanathan","doi":"10.1109/TSP.2024.3461720","DOIUrl":"10.1109/TSP.2024.3461720","url":null,"abstract":"In recent years, following the development of nested arrays and coprime arrays, several improved array constructions have been proposed to identify \u0000<inline-formula><tex-math>$mathcal{O}(N^{2})$</tex-math></inline-formula>\u0000 directions with \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 sensors and to reduce the impact of mutual coupling on the direction of arrival (DOA) estimation. However, having \u0000<inline-formula><tex-math>$mathcal{O}(N^{2})$</tex-math></inline-formula>\u0000 degrees of freedom may not be of interest, especially for large \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000. Also, a large aperture of such arrays may not be suitable when limited space is available to place the sensors. This paper presents two types of sparse array designs that can effectively handle high mutual coupling by ensuring that the coarray weights satisfy either \u0000<inline-formula><tex-math>$w(1)=0$</tex-math></inline-formula>\u0000 or \u0000<inline-formula><tex-math>$w(1)=w(2)=0$</tex-math></inline-formula>\u0000, where \u0000<inline-formula><tex-math>$w(l)$</tex-math></inline-formula>\u0000 is the number of occurrences of the difference \u0000<inline-formula><tex-math>$l$</tex-math></inline-formula>\u0000 in the set \u0000<inline-formula><tex-math>${n_{i}-n_{j}}_{i,j=1}^{N}$</tex-math></inline-formula>\u0000, and \u0000<inline-formula><tex-math>$n_{i}$</tex-math></inline-formula>\u0000 are sensors locations. In addition, several other coarray weights are small constants that do not increase with the number of sensors \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000. The arrays of the first type have an aperture of \u0000<inline-formula><tex-math>$mathcal{O}(N)$</tex-math></inline-formula>\u0000 length, making them suitable when the available aperture is restricted and the number of DOAs is also \u0000<inline-formula><tex-math>$mathcal{O}(N)$</tex-math></inline-formula>\u0000. These arrays are constructed by appropriately dilating a uniform linear array (ULA) and augmenting a few additional sensors. Despite having an aperture of \u0000<inline-formula><tex-math>$mathcal{O}(N)$</tex-math></inline-formula>\u0000 length, these arrays can still identify more than \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 DOAs. The arrays of the second type have \u0000<inline-formula><tex-math>$mathcal{O}(N^{2})$</tex-math></inline-formula>\u0000 degrees of freedom and are suitable when the aperture is not restricted. These arrays are constructed by appropriately dilating a nested array and augmenting it with several additional sensors. We compare the proposed arrays with those in the literature by analyzing their coarray properties and conducting several Monte-Carlo simulations. Unlike ULA and nested array, any sensor pair in the proposed arrays has a spacing of at least 2 units, because of the coarray hole at lag 1. In the presence of high mutual coupling, the proposed arrays can estimate DOAs with significantly smaller errors when compared to other arrays because of the reduction of coarray weight at critical small-valued la","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4444-4462"},"PeriodicalIF":4.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142236322","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}
In this paper, a design framework based on integer linear programming is proposed for optimizing sparse array structures. We resort to binary vectors to formulate the design problem for non-redundant arrays (NRA) and minimum-redundant arrays (MRA). The flexibility of the proposed framework allows for dynamic adjustment of constraints to meet various applicative requirements, e.g., to achieve desired array apertures and mitigate mutual coupling effects. The proposed framework is also extended to the design of high-order arrays associated by exploiting high-order cumulants. The effectiveness of the proposed sparse array design framework is investigated through extensive numerical analysis. A comparative analysis with closed-form solutions and integer linear programming-based array design methods confirms the superiority of the proposed design framework in terms of number of degrees of freedom (DOF) and direction of arrival (DOA) estimation accuracy.
{"title":"Sparse Array Design via Integer Linear Programming","authors":"Yangjingzhi Zhuang;Xuejing Zhang;Zishu He;Maria Sabrina Greco;Fulvio Gini","doi":"10.1109/TSP.2024.3460383","DOIUrl":"https://doi.org/10.1109/TSP.2024.3460383","url":null,"abstract":"In this paper, a design framework based on integer linear programming is proposed for optimizing sparse array structures. We resort to binary vectors to formulate the design problem for non-redundant arrays (NRA) and minimum-redundant arrays (MRA). The flexibility of the proposed framework allows for dynamic adjustment of constraints to meet various applicative requirements, e.g., to achieve desired array apertures and mitigate mutual coupling effects. The proposed framework is also extended to the design of high-order arrays associated by exploiting high-order cumulants. The effectiveness of the proposed sparse array design framework is investigated through extensive numerical analysis. A comparative analysis with closed-form solutions and integer linear programming-based array design methods confirms the superiority of the proposed design framework in terms of number of degrees of freedom (DOF) and direction of arrival (DOA) estimation accuracy.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4812-4826"},"PeriodicalIF":4.6,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540448","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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