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Learning Time-Varying Graph Signals via Koopman 通过Koopman学习时变图信号
IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-02 DOI: 10.1109/TSIPN.2025.3642225
Sivaram Krishnan;Jinho Choi;Jihong Park
A wide variety of real-world data, such as sea measurements, e.g., temperatures collected by distributed sensors and multiple unmanned aerial vehicles (UAV) trajectories, can be naturally represented as graphs, often exhibiting non-Euclidean structures. These graph representations may evolve over time, forming time-varying graphs. Effectively modeling and analyzing such dynamic graph data is critical for tasks like predicting graph evolution and reconstructing missing graph data. In this paper, we propose a framework based on the Koopman autoencoder (KAE) to handle time-varying graph data. Specifically, we assume the existence of a hidden non-linear dynamical system, where the state vector corresponds to the graph embedding of the time-varying graph signals. To capture the evolving graph structures, the graph data is first converted into a vector time series through graph embedding, representing the structural information in a finite-dimensional latent space. In this latent space, the KAE is applied to learn the underlying non-linear dynamics governing the temporal evolution of graph features, enabling both prediction and reconstruction tasks.
各种各样的现实世界数据,如海洋测量,例如由分布式传感器和多个无人机(UAV)轨迹收集的温度,可以自然地表示为图形,通常表现出非欧几里得结构。这些图形表示可能随着时间的推移而演变,形成时变图形。有效地建模和分析这些动态图数据对于预测图的演变和重建缺失的图数据等任务至关重要。本文提出了一种基于Koopman自编码器(KAE)的处理时变图数据的框架。具体来说,我们假设存在一个隐藏的非线性动力系统,其中状态向量对应于时变图信号的图嵌入。为了捕获不断变化的图结构,首先通过图嵌入将图数据转换为向量时间序列,在有限维潜在空间中表示结构信息。在这个潜在空间中,应用KAE来学习控制图特征时间演化的潜在非线性动力学,从而实现预测和重建任务。
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引用次数: 0
Distributed Event-Driven $ {ell }_infty$ Filtering in Switched Delayed Systems Over Sensor Networks Against Switching Signal Attacks 分布式事件驱动$ {ell }_infty$滤波在传感器网络上的交换延迟系统对抗交换信号攻击
IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-01 DOI: 10.1109/TSIPN.2025.3650361
Chongyi Cui;Hong Sang;Ying Zhao;Peng Wang;Shuanghe Yu;Georgi M. Dimirovski
This paper investigates distributed $ell _{infty }$ filtering problem for discrete-time switched delayed systems (DTSDSs) in sensor networks (SNs) with dynamic event-triggered communication. Given that the presence of attacks can compromise the integrity and availability of data, as well as the critical role of switching signals in shaping the behavior of switched systems, a novel event-driven distributed filter is explored in scenarios where the switching signal of the controller experiences a denial-of-service (DoS) attack, characterized by bounded attack frequency and duration. It is significant to mention that the persistent and recurrent nature of attacks compromises the transmission of switching signals, resulting in significant asynchronous discrepancies between the switching of the filtering error system (FES) and the controller. To address the asynchronous behavior induced by DoS attacks, a piecewise time-dependent Lyapunov-Krasovskii functional (PTLKF) tailored to the characteristics of DTSDSs is proposed. Subsequently, sufficient conditions with reduced conservatism are formulated to ensure the exponential stability of the FES, while also guaranteeing an enhanced $ell _{infty }$ disturbance attenuation performance. Finally, two simulation examples are provided to exemplify the superiority and applicability of the proposed filtering technique.
研究了具有动态事件触发通信的传感器网络中离散时间切换延迟系统(dtsds)的分布式$ell _{infty }$滤波问题。鉴于攻击的存在会损害数据的完整性和可用性,以及切换信号在塑造切换系统行为中的关键作用,在控制器的切换信号经历拒绝服务(DoS)攻击的情况下,探索了一种新的事件驱动分布式滤波器,其特征是攻击频率和持续时间有限。值得注意的是,攻击的持续性和周期性损害了切换信号的传输,导致滤波误差系统(FES)和控制器的切换之间存在显著的异步差异。为了解决DoS攻击引起的异步行为,提出了一种针对dtsds特征的分段时变Lyapunov-Krasovskii泛函(PTLKF)。随后,制定了降低保守性的充分条件,以确保FES的指数稳定性,同时也保证了增强的$ell _{infty }$干扰衰减性能。最后,给出了两个仿真实例,说明了所提滤波技术的优越性和适用性。
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引用次数: 0
New Diffusion Least-Mean-Squares Algorithms With Quantization and Privacy Awareness 具有量化和隐私意识的新扩散最小均方算法
IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2026-01-01 DOI: 10.1109/TSIPN.2025.3650363
Sheng Zhang;Yishu Peng;Hongyu Han;Hing Cheung So
In this paper, we devise an enhanced diffusion LMS algorithm tailored for quantization-based communication in distributed networks. Departing from conventional diffusion approaches, the proposed algorithm, called EQ-DLMS, integrates four distinct steps: (i) weight update, (ii) quantization, (iii) modified weight combination, and (iv) moving average. Through mean-square error analysis, we show how the modified combination and moving average steps impact the steady-state error bound. Notably, without adjusting the quantizer precision, the steady-state error bound avoids the typical $O(mu ^{-1})$ dependence, where $mu$ represents the step-size. However, the EQ-DLMS introduces an additional term, $O(Vert mathbf {w}^{*}Vert ^{2})$, into the error bound, where $mathbf {w}^{*}$ denotes the optimal network parameter vector. To mitigate this, we then develop an improved version of the algorithm, termed DEQ-DLMS, which employs differential quantization while preserving the modified weight combination and moving average steps. Furthermore, we extend the EQ-DLMS update mechanism to address privacy concerns. This leads to the development of an enhanced privacy-aware diffusion LMS algorithm, accompanied by a mean-square stability analysis under non-zero mean protection noise. Finally, simulations are conducted to demonstrate the effectiveness of the proposed approaches and corroborate our theoretical derivations.
在本文中,我们设计了一种针对分布式网络中基于量化的通信量身定制的增强扩散LMS算法。与传统的扩散方法不同,提出的算法称为EQ-DLMS,集成了四个不同的步骤:(i)权重更新,(ii)量化,(iii)修改权重组合,(iv)移动平均。通过均方误差分析,我们展示了修正组合和移动平均步长对稳态误差界的影响。值得注意的是,在不调整量化器精度的情况下,稳态误差界避免了典型的$O(mu ^{-1})$依赖,其中$mu$表示步长。然而,EQ-DLMS在错误界中引入了一个额外的项$O(Vert mathbf {w}^{*}Vert ^{2})$,其中$mathbf {w}^{*}$表示最优网络参数向量。为了缓解这一问题,我们开发了一种改进版本的算法,称为DEQ-DLMS,它采用微分量化,同时保留了修改的权重组合和移动平均步骤。此外,我们扩展了EQ-DLMS更新机制,以解决隐私问题。这导致了一种增强的隐私感知扩散LMS算法的发展,并伴随着非零平均保护噪声下的均方稳定性分析。最后,通过仿真验证了所提方法的有效性,并验证了我们的理论推导。
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引用次数: 0
Energy-Efficient Transmission Scheduling With Uncertainty-Aware Data Imputation for Mhealth 具有不确定性感知的移动医疗节能传输调度
IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-24 DOI: 10.1109/TSIPN.2025.3648285
Weihua Chen;Zonglin Xie;Feng Liu;Ruipeng Gao
Transmission scheduling plays a critical role in energy conservation in wireless sensor networks (WSNs), particularly in mobile health (mHealth) systems that rely on multiple distributed sensing modalities. Although recent studies have proposed approaches to balance transmission efficiency and timeliness such as periodic sleep scheduling to reduce power consumption, these strategies often result in data loss which can severely degrade real-time diagnostic accuracy. To address this issue, this paper integrates transmission scheduling with data imputation. We propose an energy-efficient software-hardware co-designed framework for mHealth systems, and investigate a Wasserstein Generative Adversarial Imputation Network (WGAIN) to recover missing data. Specifically, the WGAIN model captures heterogeneous inter-sensor correlations, temporal dependencies, and missing - data patterns through a divide-and-conquer learning strategy. Furthermore, we incorporate a dropout-based uncertainty approximation method into the imputation framework and demonstrate its theoretical equivalence to Gaussian processes under variational inference. In addition, a reinforcement learning -based algorithm is developed to dynamically schedule transmissions across heterogeneous sensing modules, with the objective of minimizing overall uncertainty at a target service time. Extensive experiments conducted on the MIT-BIH dataset, together with evaluations on a real-world system prototype, have demonstrated that our approach consistently outperforms existing methods.
在无线传感器网络(wsn)中,传输调度在节能方面起着至关重要的作用,特别是在依赖于多种分布式传感模式的移动医疗(mHealth)系统中。虽然最近的研究提出了平衡传输效率和及时性的方法,如定期睡眠调度来降低功耗,但这些策略往往会导致数据丢失,从而严重降低实时诊断的准确性。为了解决这一问题,本文将传输调度与数据输入相结合。我们为移动医疗系统提出了一种节能的软硬件协同设计框架,并研究了一种Wasserstein生成对抗Imputation网络(WGAIN)来恢复丢失的数据。具体来说,WGAIN模型通过分而治之的学习策略捕获异构的传感器间相关性、时间依赖性和缺失数据模式。此外,我们将一种基于dropout的不确定性近似方法引入到imputation框架中,并证明了它在变分推理下与高斯过程的理论等价性。此外,还开发了一种基于强化学习的算法来动态调度异构传感模块之间的传输,目的是在目标服务时间内最小化总体不确定性。在MIT-BIH数据集上进行的大量实验以及对现实世界系统原型的评估表明,我们的方法始终优于现有方法。
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引用次数: 0
Finite-Time Distributed Filtering for Multi-Rate Nonlinear Systems Suffering From Denial-of-Service Attacks: A Binary Encoding Scheme 多速率非线性系统拒绝服务攻击的有限时间分布式滤波:一种二进制编码方案
IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-24 DOI: 10.1109/TSIPN.2025.3648309
Weijian Ren;Mengdi Chang;Fengcai Huo;Chaohai Kang;Lu Ren
This paper is concerned with filtering problem related to multi-rate systems in sensor networks suffering from denial of service attacks under binary encoding scheme. Binary encoding scheme is used for scheduling the transmission of innovation between sensor nodes due to limited bandwidth. Random bit error is considered in order to reflect the existence of binary bit flipping during actual channel transmission. A switching-model approach is adopted to convert the multi-rate systems into the single-rate ones. Stochastic nonlinearity is characterized by statistical properties to enhance generality. Different occurrence probabilities of attacks in distinct channels are characterized by virtue of a group of random variables following the Bernoulli distribution. The objective of the addressed filtering issue is to design a distributed filter such that the filtering error dynamics is stochastically finite-time bounded and satisfies $H_{infty }$ performance requirement. Sufficient conditions guaranteeing the satisfaction of specified filtering performance are established with the assistance of matrix inequalities and stochastic analysis techniques. The gain parameters of the distributed filter are determined by solving certain linear matrix inequalities. Simulation outcomes validate the efficacy of the proposed filtering method.
研究了在二进制编码方案下传感器网络中多速率系统遭受拒绝服务攻击时的滤波问题。由于带宽有限,传感器节点间的创新传输采用二进制编码方式进行调度。为了反映实际信道传输过程中二进制位翻转的存在,考虑了随机误码。采用切换模型方法将多速率系统转换为单速率系统。随机非线性用统计性质来表征,以增强一般性。通过一组服从伯努利分布的随机变量来表征不同通道中攻击的不同发生概率。所解决的滤波问题的目标是设计一个分布式滤波器,使滤波误差动态是随机有限时间有界的,并满足$H_{infty }$性能要求。利用矩阵不等式和随机分析技术,建立了保证给定滤波性能满足的充分条件。分布式滤波器的增益参数是通过求解一定的线性矩阵不等式确定的。仿真结果验证了该滤波方法的有效性。
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引用次数: 0
Improved Diffusion Recursive Least Squares for Graph Signal Estimation on Distributed Network 分布式网络图信号估计的改进扩散递归最小二乘
IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-24 DOI: 10.1109/TSIPN.2025.3648322
Yi Hua;Zhangfa Wu;Hongping Gan
Streaming graph signal (GS) estimation is common in various network systems. Several graph filter algorithms have been proposed for streaming GS estimation, but they still fail to reach optimal levels. To achieve optimal performance in both estimation accuracy and convergence rate, this paper adopts the recursive least squares (RLS) method in processing GS. When the RLS algorithm is directly combined with GS, its recursive mechanism causes the estimation performance to experience severe degradation. To address this issue, a graph RLS with non-cooperation algorithm and a distributed graph diffusion RLS (DRLS) algorithm, both following the fully recursive structure of the standard RLS, are proposed first. By analyzing these two algorithms, it is found that streaming GS and graph topology are complex and variable, so the previous recursive mechanism is not suitable. Therefore, a dynamic adaptive recursive mechanism is designed, and based on this, a distributed graph improved DRLS (IDRLS) algorithm is proposed. Convergence analysis confirms that the proposed algorithm achieves mean stability and mean-square convergence at a linear rate. Furthermore, we thoroughly examine the causes of performance degradation and demonstrate the superiority of the distributed graph IDRLS algorithm. Finally, experiments, conducted on two different graphs with different levels of sparsity and real-world dataset, verify that the proposed graph IDRLS algorithm can achieve the superior estimation performance and convergence rate and be more effective than the related graph algorithms.
流图信号估计在各种网络系统中都很常见。已经提出了几种用于流GS估计的图滤波算法,但它们仍然无法达到最优水平。为了在估计精度和收敛速度上都达到最优,本文采用递推最小二乘(RLS)方法对GS进行处理。当RLS算法与GS直接结合时,其递归机制导致估计性能严重下降。为了解决这一问题,首先提出了一种非合作图RLS算法和一种分布式图扩散RLS算法,这两种算法都遵循标准RLS的完全递归结构。通过对这两种算法的分析,发现流式GS和图拓扑具有复杂多变的特点,因此之前的递归机制并不适用。为此,设计了一种动态自适应递归机制,并在此基础上提出了一种分布式图改进DRLS (IDRLS)算法。收敛性分析表明,该算法能以线性速率实现平均稳定性和均方收敛。此外,我们深入研究了性能下降的原因,并证明了分布式图IDRLS算法的优越性。最后,在两种不同稀疏度的图和真实数据集上进行了实验,验证了本文提出的图IDRLS算法具有优越的估计性能和收敛速度,并且比相关的图算法更有效。
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引用次数: 0
Full-Agent Connectivity-Preserving Secure Strategy Under Multi-Frequency Deception Attacks 多频欺骗攻击下的全代理保持连通性安全策略
IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-23 DOI: 10.1109/TSIPN.2025.3642234
Chang Liu;Zeyi Liu;Hongjing Liang;Md Altab Hossin
In this paper, a full-agent connectivity-preserving control strategy for multi-agent systems under multi-frequency deception attacks is introduced, avoiding the singularity problem caused by the initial position of the agent. This policy eliminates the need to screen agents during the initial phase and ensures the continuous presence of all agents within the communication boundaries at all times. In addition, this study considers the communication dynamics between leader and followers affected by boundaries and proposes a connectivity-preserving strategy that takes into account the full agent population. To effectively characterize the characteristics of multi-frequency attacks in practice, a time function incorporating both frequency and period information of deception attacks has been developed. This function serves to encapsulate the intentions of multi-frequency deception attackers. An extended state observer is employed to monitor and mitigate the instability caused by deception attacks. The final control framework ensures the convergence of tracking errors and the stability of the state signals.
针对多智能体系统在多频欺骗攻击下的控制策略,提出了一种全智能体保持连通性的控制策略,避免了由智能体初始位置引起的奇异性问题。此策略消除了在初始阶段筛选代理的需要,并确保所有代理始终在通信边界内持续存在。此外,本研究还考虑了受边界影响的领导者和追随者之间的沟通动态,并提出了一种考虑整个代理群体的连通性保持策略。为了在实际中有效地表征多频攻击的特征,建立了包含欺骗攻击的频率和周期信息的时间函数。此函数用于封装多频欺骗攻击者的意图。采用扩展状态观测器对欺骗攻击造成的不稳定性进行监测和减轻。最终的控制框架保证了跟踪误差的收敛性和状态信号的稳定性。
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引用次数: 0
TMGpCN: Semi-Supervised GpCN Based on Transformer Mask for Automatic Modulation Recognition TMGpCN:基于变压器掩码的半监督GpCN自动调制识别
IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-23 DOI: 10.1109/TSIPN.2025.3647210
Huali Zhu;Hua Xu;Yunhao Shi;Wanyi Gu;Xin Jia
Automatic Modulation recognition (AMR) is essential for intelligent communication receivers, with broad applications in civilian and military contexts. Deep Learning (DL) enhances recognition accuracy with high-quality, well-labeled datasets, but struggles with poorly labeled datasets or incomplete signals. To address this, we propose a semi-supervised learning approach using a $p$-Laplacian Graph Convolutional Network (GpCN) for AMR, which enhances the feature extraction capabilities by using of $p$-order convolution kernels of GCN. It is built upon the simple signal graph based on Transformer mask mechanism, which prioritize sampling points by Transformer's weight distribution. And a semi-supervised loss function reconstructed by Transformer feature reconstruction. This approach consistently yields a recognition rate of 50% with just 1% of labels on RML2016.10a dataset, outperforming the fully supervised recognition rates of existing methods. Similarly, applying the TMGpCN to a more complex dataset RML2018.01a (SNR = [−10, 10]), still achieves good performance under low-label conditions. With only 1% labeled data, the recognition accuracy for 24 types of signals reached 42.61%, which is only 8.86% lower than full supervision.
自动调制识别(AMR)是智能通信接收机的重要组成部分,在民用和军事领域有着广泛的应用。深度学习(DL)通过高质量、标记良好的数据集提高识别精度,但在标记不佳的数据集或不完整的信号时却难以识别。为了解决这个问题,我们提出了一种使用$p$-拉普拉斯图卷积网络(GpCN)进行AMR的半监督学习方法,该方法通过使用GCN的$p$阶卷积核来增强特征提取能力。它建立在基于变压器掩码机制的简单信号图的基础上,根据变压器的权重分布对采样点进行优先排序。并利用Transformer特征重构得到了一个半监督损失函数。该方法在RML2016.10a数据集上仅使用1%的标签就始终产生50%的识别率,优于现有方法的完全监督识别率。同样,将TMGpCN应用于更复杂的数据集RML2018.01a(信噪比=[−10,10]),在低标签条件下仍然可以获得良好的性能。在仅1%标记数据的情况下,对24种信号的识别准确率达到42.61%,仅比完全监督低8.86%。
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引用次数: 0
MOLENA: Analyzing the Two-Step Message Flow and Opinion Evolution Process in Social Networks
IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-19 DOI: 10.1109/TSIPN.2025.3646301
Huisheng Wang;Yuejiang Li;Yiqing Lin;H. Vicky Zhao
From mass media theory, the message flow and opinion evolution in real social networks adhere to a two-step communication process: opinion leaders first receive the messages from the message sources, and then convey their opinions to the normal agents. During this process, opinion leaders often exhibit confirmation bias by updating their opinions based on messages close to their opinions, reflecting their limited willingness to accept dissonant messages. However, there are limited prior works jointly considering the two-step communication process involving message sources, opinion leaders, and normal agents, as well as the confirmation bias of opinion leaders. In this paper, we propose a unified framework called MOLENA to analyze how messages flow and opinions evolve in the two-step communication process. In the MOLENA framework, we introduce a mathematically tractable message preference model to quantitatively describe the confirmation bias. We obtain the approximate analytical solutions of the opinion leaders' and normal agents' steady-state opinions, and theoretically analyze the influence of system parameters on their steady-state opinions. Furthermore, we examine the influence of messages on opinion leaders' steady-state opinions and study how opinion leaders influence normal agents' steady-state opinions in the two-step communication process. Finally, we validate our theoretical analysis through numerical experiments and verify the correctness of the MOLENA framework via social experiments. This study is critical to understanding how messages flow and how opinions form and evolve in real social networks, and to designing effective mechanisms to guide agents' opinions.
从大众传媒理论来看,现实社会网络中的消息流和意见演变遵循两步传播过程:意见领袖首先从消息来源接收信息,然后将自己的意见传达给正常的代理人。在这一过程中,意见领袖往往表现出确认偏误,根据与自己观点相近的信息更新自己的观点,反映出他们接受不一致信息的意愿有限。然而,考虑到信息来源、意见领袖和正常代理人的两步传播过程,以及意见领袖的确认偏见,前人的研究有限。在本文中,我们提出了一个统一的MOLENA框架来分析消息流和意见在两步传播过程中是如何演变的。在MOLENA框架中,我们引入了一个数学上易于处理的消息偏好模型来定量描述确认偏差。得到了意见领袖和普通代理人稳态意见的近似解析解,并从理论上分析了系统参数对其稳态意见的影响。此外,我们还考察了信息对意见领袖稳态意见的影响,并研究了意见领袖如何在两步传播过程中影响正常主体的稳态意见。最后,我们通过数值实验验证了理论分析,并通过社会实验验证了MOLENA框架的正确性。本研究对于理解真实社会网络中信息的流动、意见的形成和演变,以及设计有效的意见引导机制具有重要意义。
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引用次数: 0
Parameter Mapping of Distribution Substitution for Inter-Point Distances in Random Networks 随机网络中点间距离分布替换的参数映射
IF 3 3区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-12-10 DOI: 10.1109/TSIPN.2025.3642229
Shuping Dang;Jia Ye;Shuaishuai Guo;Raed Shubair;Marwa Chafii
Statistical models of inter-point distances are pivotal for analyzing and optimizing wireless communication networks and other spatial systems, such as vehicular swarms and distributed sensing networks. However, the analytical intractability of exact distance distributions often hinders closed-form performance evaluations and obscures parameter-performance relationships. To address these challenges, this paper introduces a low-complexity polynomial substitute for inter-point distance distributions and a systematic framework for parameter mapping. The framework employs two complementary mapping schemes, Relative Entropy Minimization (REM) which promotes fidelity to the original distribution in the Kullback–Leibler sense, and Mean Square Error Minimization (MSEM) which minimizes the mean squared error between the two distributions. These mappings yield parameter correspondences between the original and substitute distributions, enabling efficient and accurate approximations. The substitutes are validated on representative spatial models, preserving fidelity to the original distributions while using a low-complexity polynomial representation. This advancement facilitates closed-form evaluations and optimizations in random networks, enhancing the analytical toolkit for stochastic geometry and control theory.
点间距离的统计模型对于分析和优化无线通信网络和其他空间系统(如车辆群和分布式传感网络)至关重要。然而,精确距离分布的难以分析性往往阻碍了封闭式的性能评估,并模糊了参数-性能关系。为了解决这些问题,本文引入了一个低复杂度的多项式替代点间距离分布和一个系统的参数映射框架。该框架采用两种互补的映射方案,相对熵最小化(REM)和均方误差最小化(MSEM),前者在Kullback-Leibler意义上提高了对原始分布的保真度,后者最小化了两个分布之间的均方误差。这些映射产生了原始分布和替代分布之间的参数对应关系,从而实现了有效和准确的近似。在代表性空间模型上验证了替代品,在使用低复杂度多项式表示的同时保持了对原始分布的保真度。这一进步促进了随机网络中的封闭形式评估和优化,增强了随机几何和控制理论的分析工具包。
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引用次数: 0
期刊
IEEE Transactions on Signal and Information Processing over Networks
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