results: 研究发现，当多个fMRI时间序列数据具有多少头运动时，AR+GARCH模型可以成功地适应这些数据。此外，研究还发现了这些模型在不同的脑区之间共享波动性的现象。Abstract
Functional magnetic resonance imaging or functional MRI (fMRI) is a very popular tool used for differing brain regions by measuring brain activity. It is affected by physiological noise, such as head and brain movement in the scanner from breathing, heart beats, or the subject fidgeting. The purpose of this paper is to propose a novel approach to handling fMRI data for infants with high volatility caused by sudden head movements. Another purpose is to evaluate the volatility modelling performance of multiple dependent fMRI time series data. The models examined in this paper are AR and GARCH and the modelling performance is evaluated by several statistical performance measures. The conclusions of this paper are that multiple dependent fMRI series data can be fitted with AR + GARCH model if the multiple fMRI data have many sudden head movements. The GARCH model can capture the shared volatility clustering caused by head movements across brain regions. However, the multiple fMRI data without many head movements have fitted AR + GARCH model with different performance. The conclusions are supported by statistical tests and measures. This paper highlights the difference between the proposed approach from traditional approaches when estimating model parameters and modelling conditional variances on multiple dependent time series. In the future, the proposed approach can be applied to other research fields, such as financial economics, and signal processing. Code is available at \url{https://github.com/13204942/STAT40710}.
摘要
функциональная магнитно-резонансная томография (fMRI) 是一种非常受欢迎的工具，用于测量脑动脉的活动。它受到生理噪声的影响，例如在扫描机上的头部和脑部活动，心跳声、呼吸或试验者的不稳定。这篇论文的目的是提出一种处理高波动性fMRI数据的新方法，用于评估多个依赖关系的fMRI时间序列数据的模型性能。这篇论文分析了AR和GARCH模型，并评估了这些模型在多个依赖关系的fMRI时间序列数据中的表现。结论是，如果多个fMRI时间序列数据具有多少头部活动，那么AR + GARCH模型可以正确地适应这些数据。GARCH模型可以捕捉脑区域之间的共同波动性噪声，即头部活动引起的共同噪声。然而，没有多少头部活动的多个fMRI时间序列数据不能够正确地适应AR + GARCH模型。这些结论得到了统计测试和度量的支持。这篇论文区别于传统方法，在估计模型参数和模型conditional variance的时候。未来，该方法可以应用于其他研究领域，如金融经济和信号处理。代码可以在 中找到。
Adaptive Digital Twin for UAV-Assisted Integrated Sensing, Communication, and Computation Networks
results: 我们的方法能够均衡感知和计算功能之间的性能质量规比，同时降低计算能耗相比现有研究。Abstract
In this paper, we study a digital twin (DT)-empowered integrated sensing, communication, and computation network. Specifically, the users perform radar sensing and computation offloading on the same spectrum, while unmanned aerial vehicles (UAVs) are deployed to provide edge computing service. We first formulate a multi-objective optimization problem to minimize the beampattern performance of multi-input multi-output (MIMO) radars and the computation offloading energy consumption simultaneously. Then, we explore the prediction capability of DT to provide intelligent offloading decision, where the DT estimation deviation is considered. To track this challenge, we reformulate the original problem as a multi-agent Markov decision process and design a multi-agent proximal policy optimization (MAPPO) framework to achieve a flexible learning policy. Furthermore, the Beta-policy and attention mechanism are used to improve the training performance. Numerical results show that the proposed method is able to balance the performance tradeoff between sensing and computation functions, while reducing the energy consumption compared with the existing studies.
摘要
在本文中，我们研究了一个基于数字双（DT）的整合感知、通信和计算网络。具体来说，用户进行了雷达感知和计算卷积的同时使用同频率，而无人机（UAV）被部署以提供边缘计算服务。我们首先形ulated一个多目标优化问题，以最小化多输入多Output（MIMO）雷达的扫扫 Pattern性能和计算卷积能 consumption同时。然后，我们探索了DT的预测能力，以提供智能卷积决策。为了跟踪这个挑战，我们将原始问题重新形ulated为多个机器人Markov决策过程，并设计了一个多机器人 proximal policy optimization（MAPPO）框架，以实现 flexible learning策略。此外，我们还使用了β策略和注意力机制来提高训练性能。numerical results表明，我们的方法能够均衡感知和计算功能之间的性能交易，同时降低与已有研究相比的能 consumption。
Detecting Abrupt Change of Channel Covariance Matrix in IRS-Assisted Communication
results: 我们的提议方法通过数值结果验证了其效果。Abstract
The knowledge of channel covariance matrices is crucial to the design of intelligent reflecting surface (IRS) assisted communication. However, channel covariance matrices may change suddenly in practice. This letter focuses on the detection of the above change in IRS-assisted communication. Specifically, we consider the uplink communication system consisting of a single-antenna user (UE), an IRS, and a multi-antenna base station (BS). We first categorize two types of channel covariance matrix changes based on their impact on system design: Type I change, which denotes the change in the BS receive covariance matrix, and Type II change, which denotes the change in the IRS transmit/receive covariance matrix. Secondly, a powerful method is proposed to detect whether a Type I change occurs, a Type II change occurs, or no change occurs. The effectiveness of our proposed scheme is verified by numerical results.
摘要
知识 Channel 协方差矩阵对智能反射表（IRS）助动通信的设计非常重要。然而， Channel 协方差矩阵在实践中可能会快速变化。本信函要针对IRS协助通信中Channel 协方差矩阵变化的检测。具体来说，我们考虑了单antenna用户（UE）、IRS和多antenna基站（BS）组成的上传通信系统。我们首先将Channel 协方差矩阵变化分为两类基于它们对系统设计的影响：Type I变化，表示BS接收协方差矩阵发生变化，Type II变化表示IRS传输/接收协方差矩阵发生变化。其次，我们提出了一种强大的检测方法，能够检测Type I变化、Type II变化或者没有变化。我们的提议方案的效果得到了数值结果的验证。
Energy Efficient Robust Beamforming for Vehicular ISAC with Imperfect Channel Estimation
results: 研究结果表明，提出的算法具有良好的收敛速率，并能有效地减轻频率 estimation errors的影响。Abstract
This paper investigates robust beamforming for system-centric energy efficiency (EE) optimization in the vehicular integrated sensing and communication (ISAC) system, where the mobility of vehicles poses significant challenges to channel estimation. To obtain the optimal beamforming under channel uncertainty, we first formulate an optimization problem for maximizing the system EE under bounded channel estimation errors. Next, fractional programming and semidefinite relaxation (SDR) are utilized to relax the rank-1 constraints. We further use Schur complement and S-Procedure to transform Cramer-Rao bound (CRB) and channel estimation error constraints into convex forms, respectively. Based on the Lagrangian dual function and Karush-Kuhn-Tucker (KKT) conditions, it is proved that the optimal beamforming solution is rank-1. Finally, we present comprehensive simulation results to demonstrate two key findings: 1) the proposed algorithm exhibits a favorable convergence rate, and 2) the approach effectively mitigates the impact of channel estimation errors.
摘要
We use fractional programming and semidefinite relaxation (SDR) to relax the rank-1 constraints. We then transform the Cramer-Rao bound (CRB) and channel estimation error constraints into convex forms using Schur complement and S-Procedure, respectively.Using the Lagrangian dual function and Karush-Kuhn-Tucker (KKT) conditions, we prove that the optimal beamforming solution is rank-1. Finally, we present comprehensive simulation results to show that the proposed algorithm has a favorable convergence rate and effectively mitigates the impact of channel estimation errors.
Near-Field Positioning and Attitude Sensing Based on Electromagnetic Propagation Modeling
results: 我们的数值结果表明，我们 derivated的Ziv-Zakai bound（ZZB）可以准确预测无线信号噪声环境中 estimator 的性能。更重要的是，我们在位置估计中实现了 millimeter-level的精度，并在orientation估计中实现了0.1-level的精度。Abstract
Positioning and sensing over wireless networks are imperative for many emerging applications. However, traditional wireless channel models cannot be used for sensing the attitude of the user equipment (UE), since they over-simplify the UE as a point target. In this paper, a comprehensive electromagnetic propagation modeling (EPM) based on electromagnetic theory is developed to precisely model the near-field channel. For the noise-free case, the EPM model establishes the non-linear functional dependence of observed signals on both the position and attitude of the UE. To address the difficulty in the non-linear coupling, we first propose to divide the distance domain into three regions, separated by the defined Phase ambiguity distance and Spacing constraint distance. Then, for each region, we obtain the closed-form solutions for joint position and attitude estimation with low complexity. Next, to investigate the impact of random noise on the joint estimation performance, the Ziv-Zakai bound (ZZB) is derived to yield useful insights. The expected Cram\'er-Rao bound (ECRB) is further provided to obtain the simplified closed-form expressions for the performance lower bounds. Our numerical results demonstrate that the derived ZZB can provide accurate predictions of the performance of estimators in all signal-to-noise ratio (SNR) regimes. More importantly, we achieve the millimeter-level accuracy in position estimation and attain the 0.1-level accuracy in attitude estimation.
摘要
无线网络上的位姿探测是许多出现中的应用所必需的。然而，传统的无线通道模型无法探测用户设备（UE）的姿态，因为它们过于简化了UE为点目标。在这篇论文中，我们开发了基于电磁学理论的完整的电磁传播模型ing（EPM），以精确模拟近场通道。在无噪情况下，EPM模型确定了观察信号与UE的位置和姿态之间的非线性函数关系。为了解决近场吸引的困难，我们首先将距离域分成三个区域，由定义的相位偏移距离和间隔约束距离分割。然后，对每个区域，我们获得了具有低复杂性的闭式解决方案。接着，我们 investigate了随机噪声对共同估计性能的影响，并 derivated Ziv-Zakai bound（ZZB），以获得有用的洞察。此外，我们还提供了预期Cramér-Rao bound（ECRB），以获得简化后的关注下限表达。我们的数值结果表明， derive ZZB可以在所有信号强度（SNR）域内提供准确的性能预测。更重要的是，我们实现了百分之一级的位置估计和0.1级的姿态估计。
O-band QKD link over a multiple ONT loaded carrier-grade GPON for FTTH applications
paper_authors: N. Makris, A. Ntanos, A. Papageorgopoulos, A. Stathis, P. Konteli, I. Tsoni, G. Giannoulis, F. Setaki, T. Stathopoulos, G. Lyberopoulos, H. Avramopoulos, G. T. Kanellos, D. Syvridis
results: 研究人员成功地在多个ONT上实现了量子键分发系统的集成，并且在实际的FTTH网络中进行了多个ONT的测试和评估。Abstract
We have successfully integrated an O-band commercial Quantum-Key-Distribution (QKD) system over a lit GPON testbed that replicates a carrier-grade Fiber-to-the-Home (FTTH) optical access network with multiple ONTs to emulate real-life FTTH operational deployments.
摘要
我们已成功将 O-band 商业量子键分发（QKD）系统集成到了模拟了实际FTTH运营部署的灯光干线GPON测试基础设施中。该基础设施包括多个ONT来模拟实际FTTH网络中的多个设备。
Beampattern Design in Non-Uniform MIMO Communication
results: 研究结果表明，通过优化发射天线位置和交叉相关矩阵，可以更好地控制发射扩散 patrern，提高多输入多输出通信的性能。Abstract
In recent years and with introduction of 5G cellular network and communication, researchers have shown great interest in Multiple Input Multiple Output (MIMO) communication, an advanced technology. Many studies have examined the problem of designing the beampattern for MIMO communication using uniform arrays and the covariance-based method to concentrate the transmitted power to the users. However, this paper aims to tackle this issue in the context of non-uniform arrays. Previous authors have primarily focused on designing the transmitted beampattern based on the cross-correlation matrix of transmitted signal elements. In contrast, this paper suggests optimizing the positions of transmitted antennas along with the cross-correlation matrix. This approach is expected to produce better results.
摘要
在最近几年和5G移动通信网络的引入，研究人员对多输入多输出（MIMO）通信技术表示了极大的兴趣。许多研究都集中在多输入多输出通信中照射 patrern的设计方面，使用均匀阵列和基于协方差的方法来集中发射器的输出功率到用户。然而，这篇论文则是针对非均匀阵列进行设计照射 patrern的。以前的作者主要关注基于发射信号元素的交叉相关矩阵来设计发射 patrern。相比之下，这篇论文提议同时优化发射天线的位置和交叉相关矩阵，这种方法预计会产生更好的结果。
Multi-level Gated Bayesian Recurrent Neural Network for State Estimation
results: 在实验中，包括模拟和实际数据集，提议的闭合网络表现较为出色，比 benchmark 筛选和现有深度学习筛选方法更好。Abstract
The optimality of Bayesian filtering relies on the completeness of prior models, while deep learning holds a distinct advantage in learning models from offline data. Nevertheless, the current fusion of these two methodologies remains largely ad hoc, lacking a theoretical foundation. This paper presents a novel solution, namely a multi-level gated Bayesian recurrent neural network specifically designed to state estimation under model mismatches. Firstly, we transform the non-Markov state-space model into an equivalent first-order Markov model with memory. It is a generalized transformation that overcomes the limitations of the first-order Markov property and enables recursive filtering. Secondly, by deriving a data-assisted joint state-memory-mismatch Bayesian filtering, we design a Bayesian multi-level gated framework that includes a memory update gate for capturing the temporal regularities in state evolution, a state prediction gate with the evolution mismatch compensation, and a state update gate with the observation mismatch compensation. The Gaussian approximation implementation of the filtering process within the gated framework is derived, taking into account the computational efficiency. Finally, the corresponding internal neural network structures and end-to-end training methods are designed. The Bayesian filtering theory enhances the interpretability of the proposed gated network, enabling the effective integration of offline data and prior models within functionally explicit gated units. In comprehensive experiments, including simulations and real-world datasets, the proposed gated network demonstrates superior estimation performance compared to benchmark filters and state-of-the-art deep learning filtering methods.
摘要
bayesian滤波的优点取决于先前模型的完整性，而深度学习具有从线上数据学习模型的优势。然而，现有的这两种方法的结合仍然是广义的，缺乏理论基础。这篇论文提出了一种新的解决方案，即一种多级闭合泛bayesian循环神经网络，专门用于状态估计下的模型差异。首先，我们将非马歇维状态空间模型转换成一个等效的首级Markov模型，以掌握时间序列的特征。这是一种通用的转换方法，可以超越首级Markov性质的限制，并允许递归滤波。其次，通过 derive一种基于数据助记的共同状态记忆滤波，我们设计了一种bayesian多级闭合框架，包括一个记忆更新门、一个状态预测门和一个观测差异补偿门。在这个框架内，我们使用Gaussian approximation实现滤波过程，考虑计算效率。最后，我们设计了相关的内部神经网络结构和端到端训练方法。bayesian滤波理论增强了我们提议的闭合网络的解释能力，使得可以有效地结合在线数据和先前模型内部functionallyExplicit的闭合单元。在广泛的实验中，包括仿真和实际数据，我们的闭合网络示出了与标准滤波器和深度学习滤波方法相比较好的估计性能。
Max-min Rate Optimization of Low-Complexity Hybrid Multi-User Beamforming Maintaining Rate-Fairness
results: 我们开发了一种基于 convexsolver 算法的方法，可以逐步解决相同的 beamformer 大小的几何问题。我们还引入了 soft max-min rate 目标函数，并开发了可扩展的优化算法。我们的实验结果表明，soft max-min rate 优化不仅可以达到最小用户速率的最低值，而且还可以实现与 sum-rate 最大化的同等总吞吐率。因此，我们的概念的束 beamforming 设计可以提供一种新的同时实现高个用户质量服务和高总网络吞吐率的技术。Abstract
A wireless network serving multiple users in the millimeter-wave or the sub-terahertz band by a base station is considered. High-throughput multi-user hybrid-transmit beamforming is conceived by maximizing the minimum rate of the users. For the sake of energy-efficient signal transmission, the array-of-subarrays structure is used for analog beamforming relying on low-resolution phase shifters. We develop a convexsolver based algorithm, which iteratively invokes a convex problem of the same beamformer size for its solution. We then introduce the soft max-min rate objective function and develop a scalable algorithm for its optimization. Our simulation results demonstrate the striking fact that soft max-min rate optimization not only approaches the minimum user rate obtained by max-min rate optimization but it also achieves a sum rate similar to that of sum-rate maximization. Thus, the soft max-min rate optimization based beamforming design conceived offers a new technique of simultaneously achieving a high individual quality-of-service for all users and a high total network throughput.
摘要
“考虑一个无线网络，用于服务多个用户，运行在毫米波频率或子teraHz频率带之中的基站。我们提出了一种高通量多用户混合传输射频几何，通过将最大化最低用户速率来实现。为了节省能源，我们使用了一个组件-subarray结构，实现了低分辨率相位调整器的对称传输。我们开发了一个基于convex solver的算法，逐步解决一个具有相同对称传输组件大小的问题。我们然后引入了软max-min率目标函数，并开发了可扩展的数值估算法来优化它。我们的实验结果显示，soft max-min率优化不仅可以接近最小用户速率的最大化优化，而且还可以 дости得一个相似于sum-rate最大化的总网络吞吐量。因此，我们的设计提案可以提供一种新的技术，即同时确保所有用户的个人质量服务水准高，并且确保网络吞吐量高。”
Reducing the impact of non-ideal PRBS on microwave photonic random demodulators by low biasing the optical modulator via PRBS amplitude compression
paper_authors: Shiyang Liu, Yang Chen for: 这篇论文旨在解决对microwave photonic random demodulators (RDs)中非理想pseudo-random binary sequence (PRBS)的影响。methods: 本研究提出了一种新的方法，利用lower amplitude PRBS来对光学模拟器进行偏好偏移，以减少非理想PRBS对microwave photonic RDs的影响。results: 实验结果显示，这种方法可以降低重建误差达85%。此方法可以对RD-based photonics-assisted compressed sensing (CS)系统中PRBS的要求进行重大减少，提供一个可行的解决方案，从而降低系统实现的复杂度和成本。Abstract
A novel method for reducing the impact of non-ideal pseudo-random binary sequence (PRBS) on microwave photonic random demodulators (RDs) in a photonics-assisted compressed sensing (CS) system is proposed. Different from the commonly used method that switches the bias point of the optical modulator in the RD between two quadrature transmission points to mix the signal to be sampled and the PRBS, this method employs a PRBS with lower amplitude to low bias the optical modulator so that the impact of non-ideal PRBS on microwave photonic RDs can be greatly reduced by compressing the amplitude of non-ideal parts of the PRBS. An experiment is performed to verify the concept. The optical modulator is properly low-biased via PRBS amplitude compression. The data rate and occupied bandwidth of the PRBS are 500 Mb/s and 1 GHz, while the multi-tone signals with a maximum frequency of 100 MHz are sampled at an equivalent sampling rate of only 50 MSa/s. The results show that the reconstruction error can be reduced by up to 85%. The proposed method can significantly reduce the requirements for PRBS in RD-based photonics-assisted CS systems, providing a feasible solution for reducing the complexity and cost of system implementation.
摘要
一种新的方法可以减少光学抽象推定系统中pseudo-random binary sequence(PRBS)的影响。与常用的方法不同，这种方法使用低 amplitud PRBS来压缩光学模拟器的偏好点，从而减少非理想PRBS对微波光学RD的影响。一个实验证明了这个概念。通过PRBS压缩 amplitude来低 bias光学模拟器。数据率和占用频谱带宽为500Mb/s和1GHz，而多谱信号的最大频率为100MHz， Sampled at an equivalent sampling rate of only 50MSa/s。结果显示，可以将重建错误降低到85%。提议的方法可以减少RD基于光学抽象推定系统中PRBS的复杂性和成本，提供一个可行的解决方案。