methods: 本论文提出了两种通annel估算方法：两stage RIS OFF-ON方法和增强alternating least squares（E-ALS）方法。两种方法都利用tensor模型的结构来分离估算所有通信频道，而且比传统的最小二乘（LS）方法更高效。
results: 数值仿真结果表明，E-ALS方法可以提供最高精度的估算，但只有与两stage方法的运行时间相似。Abstract
We consider a narrowband MIMO reconfigurable intelligent surface (RIS)-assisted wireless communication system and use tensor signal modelling techniques to individually estimate all communication channels including the non-RIS channels (direct path) and decoupled RIS channels. We model the received signal as a third-order tensor composed of two CANDECOMP/PARAFAC decomposition terms for the non-RIS and the RIS-assisted links, respectively, and we propose two channel estimation methods based on an iterative alternating least squares (ALS) algorithm: The two-stage RIS OFF-ON method estimates each of the non-RIS and RIS-assisted terms in two pilot training stages, whereas the enhanced alternating least squares (E-ALS) method improves upon the ALS algorithm to jointly estimate all channels over the full training duration. A key benefit of both methods compared to the traditional least squares (LS) solution is that they exploit the structure of the tensor model to obtain decoupled estimates of all communication channels. We provide the computational complexities to obtain each of the channel estimates for our two proposed methods. Numerical simulations are used to evaluate the accuracy and verify the computational complexities of the proposed two-stage RIS OFF-ON, and E-ALS, and compare them to the traditional LS methods. Results show that E-ALS will obtain the most accurate estimate while only having a slightly higher run-time than the two-stage method.
摘要
我们考虑一个宽频段多Input多Output（MIMO）启动智能表面（RIS）协助无线通信系统，并使用张量信号模型技术来分别估算所有通信频道，包括非RIS频道（直接路径）和分离的RIS频道。我们模型接收信号为一个第三个张量，由两个CANDECOMP/PARAFAC分解项组成，一个是非RIS链和RIS协助链的两个分别链路。我们提出了两种通道估算方法，基于轮换最小二乘（ALS）算法：一是两stage RIS OFF-ON方法，它在两个预训练阶段中分别估算非RIS和RIS协助链的每个通信频道，而另一种是提高ALS算法的增强ALS方法，它在全duration训练时间内同时估算所有通信频道。与传统最小二乘（LS）方法相比，两种方法都利用张量模型的结构来获得分离的所有通信频道估算。我们提供了每个通道估算的计算复杂度。我们通过数值仿真来评估和验证我们的两种提议方法的准确性和计算复杂度，并与传统LS方法进行比较。结果表明，增强ALS方法将获得最准确的估算，只有与两stage方法的运行时间有些着。
Effective filtering approach for joint parameter-state estimation in SDEs via Rao-Blackwellization and modularization
paper_authors: Zhou Fang, Ankit Gupta, Mustafa Khammash
For: Joint parameter-state estimation in stochastic differential equations (SDEs)* Methods: Rao-Blackwellization and modularization* Results: Superior performance compared to existing approaches, with reduced computational complexity and mitigated issues of sample degeneracy and information loss.Here’s the Simplified Chinese text for each point:
for: 用于joint参数-状态估计在涨见微分方程（SDEs）中
methods: 使用Rao-Blackwellization和模块化
results: 与现有方法相比，表现更佳，具有减少计算复杂性和消除样本缺乏和信息损失等问题。Abstract
Stochastic filtering is a vibrant area of research in both control theory and statistics, with broad applications in many scientific fields. Despite its extensive historical development, there still lacks an effective method for joint parameter-state estimation in SDEs. The state-of-the-art particle filtering methods suffer from either sample degeneracy or information loss, with both issues stemming from the dynamics of the particles generated to represent system parameters. This paper provides a novel and effective approach for joint parameter-state estimation in SDEs via Rao-Blackwellization and modularization. Our method operates in two layers: the first layer estimates the system states using a bootstrap particle filter, and the second layer marginalizes out system parameters explicitly. This strategy circumvents the need to generate particles representing system parameters, thereby mitigating their associated problems of sample degeneracy and information loss. Moreover, our method employs a modularization approach when integrating out the parameters, which significantly reduces the computational complexity. All these designs ensure the superior performance of our method. Finally, a numerical example is presented to illustrate that our method outperforms existing approaches by a large margin.
摘要
This paper proposes a novel and effective approach for joint parameter-state estimation in SDEs through Rao-Blackwellization and modularization. Our method operates in two layers: the first layer estimates the system states using a bootstrap particle filter, and the second layer marginalizes out system parameters explicitly. By avoiding the need to generate particles representing system parameters, our method mitigates the associated problems of sample degeneracy and information loss. Additionally, our method employs a modularization approach when integrating out the parameters, which significantly reduces the computational complexity. These designs ensure the superior performance of our method.To demonstrate the effectiveness of our approach, we provide a numerical example that shows our method outperforms existing methods by a large margin.
Regularized Shannon sampling formulas related to the special affine Fourier transform
results: 对比于Shannon抽取系列，这种减震抽取方法具有加速衰减的抽取误差和在噪声存在时的数值稳定性。数值实验证明了理论结论。Abstract
In this paper, we present new regularized Shannon sampling formulas related to the special affine Fourier transform (SAFT). These sampling formulas use localized sampling with special compactly supported window functions, namely B-spline, sinh-type, and continuous Kaiser-Bessel window functions. In contrast to the Shannon sampling series for SAFT, the regularized Shannon sampling formulas for SAFT possesses an exponential decay of the approximation error and are numerically robust in the presence of noise, if certain oversampling condition is fulfilled. Several numerical experiments illustrate the theoretical results.
摘要
在这篇论文中，我们提出了新的减杂化Shannon抽取方法，与特殊直交傅立卷变换 (SAFT) 相关。这些抽取方法使用了本地化抽取，使用特殊压缩支持的窗函数，包括B-spline、sinh型和连续凯зер-贝塞尔窗函数。与Shannon抽取系列不同，我们的减杂化Shannon抽取方法具有辐射衰减的扩散误差，在噪声存在时是数值稳定的，只要满足certain oversampling condition。我们在数值实验中证明了这些理论结果。
A Leakage-based Method for Mitigation of Faulty Reconfigurable Intelligent Surfaces
paper_authors: N. Moghadas Gholian, M. Rossanese, P. Mursia, A. Garcia-Saavedra, A. Asadi, V. Sciancalepore, X. Costa-Pérez for:这篇论文旨在解决智能表面重配置技术在未来5G无线网络中的一个潜在问题，即不良信号散射。methods:本文提出了两种简单 yet effective的算法，它们基于最大化信号泄漏和噪声比率（SLNR）在预定的二维空间中，并适用于完美通道状态信息（CSI）和部分CSI情况下。results:数值和全波仿真结果表明，这两种算法可以提供更大的补偿效果，比对泄漏无法和参照方案。Abstract
Reconfigurable Intelligent Surfaces (RISs) are expected to be massively deployed in future beyond-5th generation wireless networks, thanks to their ability to programmatically alter the propagation environment, inherent low-cost and low-maintenance nature. Indeed, they are envisioned to be implemented on the facades of buildings or on moving objects. However, such an innovative characteristic may potentially turn into an involuntary negative behavior that needs to be addressed: an undesired signal scattering. In particular, RIS elements may be prone to experience failures due to lack of proper maintenance or external environmental factors. While the resulting Signal-to-Noise-Ratio (SNR) at the intended User Equipment (UE) may not be significantly degraded, we demonstrate the potential risks in terms of unwanted spreading of the transmit signal to non-intended UE. In this regard, we consider the problem of mitigating such undesired effect by proposing two simple yet effective algorithms, which are based on maximizing the Signal-to-Leakage- and-Noise-Ratio (SLNR) over a predefined two-dimensional (2D) area and are applicable in the case of perfect channel-state-information (CSI) and partial CSI, respectively. Numerical and full-wave simulations demonstrate the added gains compared to leakage-unaware and reference schemes.
摘要
“复 configurable 智能表面”（RIS）在未来第5代无线网络中大规模部署，感谢其可以通过程序修改媒体传播环境，具有低成本和低维护性。实际上，它们可能被实现在建筑物外墙或在移动物体上。然而，这种创新特点可能会变成不良行为：不需要的信号散射。具体来说，RIS元素可能因缺乏正确维护或外部环境因素而出现故障。尽管传输信号的噪声比（SNR）在意图用户设备（UE）上不会受到显著干扰，但我们表示这种不良影响的风险。在这种情况下，我们考虑了 mitigate 这种不良效果的两种简单 yet effective 算法，它们基于在预定的二维（2D）区域中 maximize 信号干扰比率（SLNR），并在完美通道状态信息（CSI）和部分 CSI 情况下都适用。数值和全波 simulations 表明，相比于干扰无法和参照方案，这两种方法增加了额外的优势。
Generating HSR Bogie Vibration Signals via Pulse Voltage-Guided Conditional Diffusion Model
for: Fault diagnosis of high-speed rail (HSR) bogies
methods: Pulse Voltage-Guided Conditional Diffusion Model (VGCDM)
results: Outperforms other generative models, achieving the best RSME, PSNR, and FSCS indicators, demonstrating its superiority in generating HSR bogie vibration signals.Abstract
Generative Adversarial Networks (GANs) for producing realistic signals, have substantially improved fault diagnosis algorithms in various Internet of Things (IoT) systems. However, challenges such as training instability and dynamical inaccuracy limit their utility in high-speed rail (HSR) bogie fault diagnosis. To address these challenges, we introduce the Pulse Voltage-Guided Conditional Diffusion Model (VGCDM). Unlike traditional implicit GANs, VGCDM adopts a sequential U-Net architecture, facilitating multi-phase denoising diffusion for generation, which bolsters training stability and mitigate convergence issues. VGCDM also incorporates control pulse voltage by cross-attention mechanism to ensure the alignment of vibration with voltage signals, enhancing the Conditional Diffusion Model's progressive controlablity. Consequently, solely straightforward sampling of control voltages, ensuring the efficient transformation from Gaussian Noise to vibration signals. This adaptability remains robust even in scenarios with time-varying speeds. To validate the effectiveness, we conducted two case studies using SQ dataset and high-simulation HSR bogie dataset. The results of our experiments unequivocally confirm that VGCDM outperforms other generative models, achieving the best RSME, PSNR, and FSCS, showing its superiority in conditional HSR bogie vibration signal generation. For access, our code is available at https://github.com/xuanliu2000/VGCDM.
摘要
Generative Adversarial Networks (GANs) 已经广泛应用于许多互联网东西 (IoT) 系统中，以生成实际的信号，提高了问题诊断算法的准确性。然而，在高速铁路 (HSR) bogie 问题诊断中，GANs 受到许多挑战，如训练不稳定和动态不准确。为了解决这些挑战，我们提出了普ulse Voltage-Guided Conditional Diffusion Model (VGCDM)。VGCDM 采用了序列 U-Net 架构，实现多个阶段的净化扩散，从而提高训练稳定性和抑制混合问题。VGCDM 还通过cross-attention机制来控制普ulse电压，确保振荡与电压信号的对齐，提高 Conditional Diffusion Model 的进行控制性。因此，只需单纯地采样控制电压，以确保高效地将 Gaussian Noise 转化为振荡信号。这种适应性能够在时间变化的情况下保持稳定。为证明效果，我们在 SQ 数据集和高 simulate HSR bogie 数据集上进行了两个案例研究。结果表明，VGCDM 明显超过了其他生成模型，实现最佳 RSME、PSNR 和 FSCS，证明其在Conditional HSR bogie 振荡信号生成中的优越性。对于更多信息，请参考我们的 GitHub 仓库 。
Intelligent Surface Empowered Integrated Sensing and Communication: From Coexistence to Reciprocity
results: 研究发现，IRS可以有效地扩大S&C覆盖范围和控制通信频率的度量，从而实现更高的集成增益。同时，研究还发现了不同部署策略之间的干扰关系，并提出了一些有前途的IRS增强ISAC的方向。Abstract
Integrated sensing and communication (ISAC) has attracted growing interests for sixth-generation (6G) and beyond wireless networks. The primary challenges faced by highly efficient ISAC include limited sensing and communication (S&C) coverage, constrained integration gain between S&C under weak channel correlations, and unknown performance boundary. Intelligent reflecting/refracting surfaces (IRSs) can effectively expand S&C coverage and control the degree of freedom of channels between the transmitters and receivers, thereby realizing increasing integration gains. In this work, we first delve into the fundamental characteristics of IRS-empowered ISAC and innovative IRS-assisted sensing architectures. Then, we discuss various objectives for IRS channel control and deployment optimization in ISAC systems. Furthermore, the interplay between S&C in different deployment strategies is investigated and some promising directions for IRS enhanced ISAC are outlined.
摘要
Integrated sensing and communication (ISAC) 在 sixth-generation (6G) 和更高版本无线网络中吸引了增长的关注。主要挑战包括有限的感知和通信 (S&C) 覆盖率、弱通道相关性下的约束集成增益，以及未知性能边界。智能反射/折射 поверхност (IRS) 可以有效地扩大 S&C 覆盖率，控制通信道之间的度量自由度，从而实现增加集成增益。在这种工作中，我们首先探讨了 ISAC 的基本特点和创新的 IRS 感知架构。然后，我们讨论了 ISAC 系统中 IRS 通道控制和部署优化的多种目标。此外，我们还研究了不同部署策略下的 S&C 间的互动，并提出了一些潜在的 IRS 增强 ISAC 的方向。
Deriving Characteristic Mode Eigenvalue Behavior Using Subduction of Group Representations
results: 该研究发现，在三维结构中，原来的交叠轨迹将分裂成多个不同的轨迹，形成了一种 Split Trace Crossing Avoidance（STCA）。这一发现可以解释三维结构中观察到的凹槽轨迹。此外，该研究还验证了这种知识的实用性，通过一个示例式天线设计，并在设计中选择了STCA在目标频率范围外，以避免输入匹配和远场图像的频率稳定性的负面影响。Abstract
A method to derive features of modal eigenvalue traces from known and understood solutions is proposed. It utilizes the concept of subduction from point group theory to obtain the symmetry properties of a target structure from those of a structure with a higher order of symmetry. This is applied exemplary to the analytically known characteristic modes (CMs) of the spherical shell. The eigenvalue behavior of a cube in free space and a cuboid on a perfectly electrically conducting plane are continuously derived from this. In this process, formerly crossing eigenvalue traces are found to split up, forming a split trace crossing avoidance (STCA). This finding is used to explain indentations in eigenvalue traces observed for three-dimensional structures, that are of increasing interest in recent literature. The utility of this knowledge is exemplified through a demonstrator antenna design. The dimensions of the antenna structure are chosen so the STCA is outside the target frequency range, avoiding negative impacts on input matching and the frequency stability of the far field patterns.
摘要
提出一种方法，用于从已知和理解的解的特征Derive特征 tracestracestructure的模态值轨迹。该方法利用点组 тео리 ahp 的投射 Property来获取目标结构的 симметry 属性，从而 derive the symmetry properties of a target structure from those of a structure with a higher order of symmetry. This is applied exemplary to the analytically known characteristic modes (CMs) of the spherical shell. The eigenvalue behavior of a cube in free space and a cuboid on a perfectly electrically conducting plane are continuously derived from this. In this process, formerly crossing eigenvalue traces are found to split up, forming a split trace crossing avoidance (STCA). This finding is used to explain indentations in eigenvalue traces observed for three-dimensional structures, that are of increasing interest in recent literature. The utility of this knowledge is exemplified through a demonstrator antenna design. The dimensions of the antenna structure are chosen so the STCA is outside the target frequency range, avoiding negative impacts on input matching and the frequency stability of the far field patterns.
On the Semi-Blind Mutually Referenced Equalizers for MIMO Systems
results: SB-MRE算法在比较其他线性算法（MMSE、ZF、MRE）的 simulate 结果中，在训练负担和复杂度方面表现出色，可以为无线通信系统中的频率响应和信号识别率提供一个有望的解决方案。Abstract
Minimizing training overhead in channel estimation is a crucial challenge in wireless communication systems. This paper presents an extension of the traditional blind algorithm, called "Mutually referenced equalizers" (MRE), specifically designed for MIMO systems. Additionally, we propose a novel semi-blind method, SB-MRE, which combines the benefits of pilot-based and MRE approaches to achieve enhanced performance while utilizing a reduced number of pilot symbols. Moreover, the SB-MRE algorithm helps to minimize complexity and training overhead and to remove the ambiguities inherent to blind processing. The simulation results demonstrated that SB-MRE outperforms other linear algorithms, i.e., MMSE, ZF, and MRE, in terms of training overhead symbols and complexity, thereby offering a promising solution to address the challenge of minimizing training overhead in channel estimation for wireless communication systems.
摘要
减少通信系统中的训练负担是无线通信系统中的一项重要挑战。这篇论文提出了基于多input多output（MIMO）系统的传统盲目算法扩展——共见平衡器（Mutually Referenced Equalizers，MRE）。此外，我们还提出了一种新的半盲目方法，半盲目MRE（SB-MRE），该方法结合了徽标基于和MRE方法的优点，以实现更高的性能，同时减少了徽标数量。此外，SB-MRE算法可以减少复杂性和训练负担，并解决盲目处理中存在的不确定性。实验结果表明，SB-MRE在训练负担符号和复杂性方面与其他线性算法（MMSE、ZF、MRE）相比，表现更好，因此可以有效地解决无线通信系统中的训练负担减少挑战。
Improving MIMO channel estimation via receive power feedback
results: 研究显示，使用相应的MMSE可以提高估计精度，而使用MAP估计器的有用性取决于操作的SNR。Abstract
Estimating the channel state is known to be an important problem in wireless networks. To this end, it matters to exploit all the available information to improve channel estimation accuracy as much as possible. It turns out that the problem of exploiting the information associated with the receive power feedback (e.g., the received signal strength indicator -RSSI-) has not been identified and solved; in this setup, the transmitter is assumed to receive feedback from all the receivers in presence. As shown in this paper, to solve this problem, classical estimation tools can be used. Using the corresponding MMSE is shown to be always beneficial, whereas the relevance of using the MAP estimator would depend on the operating SNR.
摘要
<>将文本翻译成简化中文。<>无线网络中估算通道状态是一个重要的问题。为此，需要尽可能地利用所有可用的信息来提高通道估算精度。实际上，受到返回Feedback（如接收信号强度指示器（RSSI））的信息利用问题尚未得到解决。在这种设置下，传输器接收来自所有接收器的反馈。根据这篇论文，可以使用经典的估算工具来解决这个问题。使用相应的MMSE是有利的，而使用MAP估算器的有用性则取决于操作SNR。