results: 得到了一种几何特征化的不唯一解问题的情况，证明了这种情况可以发生在任何维度和卫星数量下，推翻了一些已有的 conjectures。此外，提供了一个证明：当m≥n+2时，用户位置 almost all情况下有唯一解；当m≥2n+2时， almost all卫星配置都可以确保用户位置有唯一解。Abstract
We provide a new algebraic solution procedure for the global positioning problem in $n$ dimensions using $m$ satellites. We also give a geometric characterization of the situations in which the problem does not have a unique solution. This characterization shows that such cases can happen in any dimension and with any number of satellites, leading to counterexamples to some open conjectures. We fill a gap in the literature by giving a proof for the long-held belief that when $m \ge n+2$, the solution is unique for almost all user positions. Even better, when $m \ge 2n+2$, almost all satellite configurations will guarantee a unique solution for all user positions. Some of our results are obtained using tools from algebraic geometry.
摘要
我们提供了一种新的代数解决方法，用于在 $n$ 维空间中解决全球定位问题，使用 $m$ 颗卫星。我们还给出了一种几何特征化，用于描述无Unique解的情况。这个特征化表明，这种情况可以在任何维度和任何卫星数量下出现，这些counterexample 解决了一些长期存在的 conjecture。我们填充了文献中的一个空白，证明了当 $m \ge n+2$ 时，用户位置 almost all 情况下存在唯一解。更好的是，当 $m \ge 2n+2$ 时，大多数卫星配置都可以保证所有用户位置的唯一解。一些我们的结果使用了代数几何工具。
Histogram-less LiDAR through SPAD response linearization
results: 该方法被validate通过广泛的数学分析和数值 Monte Carlo 模型，其预测与实际测量设置中的证明一致，并在较高的背景干扰下达到3.8米的距离。Abstract
We present a new method to acquire the 3D information from a SPAD-based direct-Time-of-Flight (d-ToF) imaging system which does not require the construction of a histogram of timestamps and can withstand high flux operation regime. The proposed acquisition scheme emulates the behavior of a SPAD detector with no distortion due to dead time, and extracts the Tof information by a simple average operation on the photon timestamps ensuring ease of integration in a dedicated sensor and scalability to large arrays. The method is validated through a comprehensive mathematical analysis, whose predictions are in agreement with a numerical Monte Carlo model of the problem. Finally, we show the validity of the predictions in a real d-ToF measurement setup under challenging background conditions well beyond the typical pile-up limit of 5% detection rate up to a distance of 3.8 m.
摘要
我们提出了一种新的方法，可以从 SPAD 基于 direct-Time-of-Flight（d-ToF）图像系统中获取三维信息，不需要构建历史gram的时间排序和可以承受高流动操作模式。我们的获取方案模拟 SPAD 探测器无损时间延迟的行为，通过简单的平均操作来提取 Tof 信息，以便易于集成到专门的感器中和可扩展到大型数组。我们通过了全面的数学分析，其预测与数字 Monte Carlo 模型的预测一致。最后，我们在实际 d-ToF 测量设置下表明了该预测的有效性，包括耗送背景条件超出 Typical 堆叠限制的5% 检测率至 3.8 米之距离。
DNFS-VNE: Deep Neuro-Fuzzy System-Driven Virtual Network Embedding Algorithm
results: 实验结果表明，提出的DNFS-based VNE算法可以减少虚拟网络的coupling度，提高服务质量和多样化性。同时，DNFS可以帮助找到更加精准的虚拟网络嵌入。Abstract
By decoupling substrate resources, network virtualization (NV) is a promising solution for meeting diverse demands and ensuring differentiated quality of service (QoS). In particular, virtual network embedding (VNE) is a critical enabling technology that enhances the flexibility and scalability of network deployment by addressing the coupling of Internet processes and services. However, in the existing works, the black-box nature of deep neural networks (DNNs) limits the analysis, development, and improvement of systems. In recent times, interpretable deep learning (DL) represented by deep neuro-fuzzy systems (DNFS) combined with fuzzy inference has shown promising interpretability to further exploit the hidden value in the data. Motivated by this, we propose a DNFS-based VNE algorithm that aims to provide an interpretable NV scheme. Specifically, data-driven convolutional neural networks (CNNs) are used as fuzzy implication operators to compute the embedding probabilities of candidate substrate nodes through entailment operations. And, the identified fuzzy rule patterns are cached into the weights by forward computation and gradient back-propagation (BP). In addition, the fuzzy rule base is constructed based on Mamdani-type linguistic rules using linguistic labels. Finally, the effectiveness of evaluation indicators and fuzzy rules is verified by experiments.
摘要
通过卸载基础资源 Coupling, 网络虚拟化（NV）是一种有前途的解决方案，用于满足多样化的需求并确保不同的服务质量（QoS）。特别是虚拟网络嵌入（VNE）是一种关键的促进技术，它提高了网络部署的灵活性和扩展性，并且可以解决互联网过程和服务的卷积问题。然而，在现有的工作中，深度神经网络（DNN）的黑盒特性限制了系统分析、开发和改进的能力。在最近的时间里，可解释的深度学习（DL），表示的深度神经网络系统（DNFS），与杂化推理相结合，已经显示出了可解释性的提高，以便更好地利用数据中隐藏的值。驱动了这一点，我们提出了一种基于 DNFS 的 VNE 算法，目的是提供一种可解释的 NV 方案。具体来说，用户提供的数据驱动的卷积神经网络（CNN）被用作杂化推理操作来计算候选基础节点的嵌入概率。而identified的杂化规则模式被缓存在权重中通过前向计算和反向传播（BP）。此外，基于 Mamdani 类型语言规则的语言标签建立了杂化规则基。最后，实验证明了评价指标和杂化规则的有效性。
Cell-Free Massive MIMO for ISAC: Access Point Operation Mode Selection and Power Control
results: 我们的数字结果表明，提出的AP操作模式选择与功率控制可以在给定探测要求下显著改善通信性能。Abstract
This paper considers a cell-free massive multipleinput multiple-output (MIMO) integrated sensing and communication (ISAC) system, where distributed MIMO access points (APs) are used to jointly serve the communication users and detect the presence of a single target. We investigate the problem of AP operation mode selection, wherein some APs are dedicated for downlink communication, while the remaining APs are used for sensing purposes. Closed-form expressions for the individual spectral efficiency (SE) and mainlobe-to-average-sidelobe ratio (MASR) are derived, which are respectively utilized to assess the communication and sensing performances. Accordingly, a maxmin fairness problem is formulated and solved, where the minimum SE of the users is maximized, subject to the per-AP power constraints as well as sensing MASR constraint. Our numerical results show that the proposed AP operation mode selection with power control can significantly improve the communication performance for given sensing requirements.
摘要
本文考虑了一个无细胞大规模多输入多输出（MIMO）集成感知通信（ISAC）系统，其中分布式MIMO访问点（AP）被用来共同服务通信用户和检测目标的存在。我们研究了AP操作模式选择问题，其中一些AP专门用于下降通信，剩下的AP用于探测用途。我们 derivatedclosed-form表达式，用于评估通信和探测性能。根据这些表达式，我们建立了最大最小公正问题，其中最小的用户SE被最大化，同时保证每个AP的功率限制以及探测MASR限制。我们的数字结果表明，提出的AP操作模式选择策略可以显著提高给定的探测要求下的通信性能。
Survey on Near-Space Information Networks: Channel Modeling, Networking, and Transmission Perspectives
results: 提供最新的NSIN技术发展和应用场景，包括通信协议和网络部署方法，以及空中平台不稳定运动对antenna数组阶段延迟的影响。Abstract
Near-space information networks (NSIN) composed of high-altitude platforms (HAPs), high- and low-altitude unmanned aerial vehicles (UAVs) are a new regime for providing quickly, robustly, and cost-efficiently sensing and communication services. Precipitated by innovations and breakthroughs in manufacturing, materials, communications, electronics, and control technologies, NSIN have emerged as an essential component of the emerging sixth-generation of mobile communication systems. This article aims at providing and discussing the latest advances in NSIN in the research areas of channel modeling, networking, and transmission from a forward-looking, comparative, and technological evolutionary perspective. In this article, we highlight the characteristics of NSIN and present the promising use-cases of NSIN. The impact of airborne platforms' unstable movements on the phase delays of onboard antenna arrays with diverse structures is mathematically analyzed. The recent advancements in HAP channel modeling are elaborated on, along with the significant differences between HAP and UAV channel modeling. A comprehensive review of the networking technologies of NSIN in network deployment, handoff management, and network management aspects is provided. Besides, the promising technologies and communication protocols of the physical layer, medium access control (MAC) layer, network layer, and transport layer of NSIN for achieving efficient transmission over NSIN are overviewed. Finally, we outline some open issues and promising directions of NSIN deserved for future study and discuss the corresponding challenges.
摘要
近空信息网络（NSIN）由高空平台（HAP）、高空和低空无人机（UAV）组成，是一种新的服务提供方式，具有快速、可靠、成本效益的优势。由于制造技术、材料、通信、电子和控制技术的进步，NSIN已经成为第六代移动通信系统的重要组成部分。本文旨在提供和讨论最新的NSIN研究进展，包括通道模型、网络和传输技术。本文特点出了NSIN的特点，并提出了NSIN的有前途的应用场景。文中还分析了机上天线阵列的相位延迟问题，并对高空平台通道模型进行了详细介绍。此外，本文还提供了NSIN网络部署、承接管理和网络管理方面的全面评论，以及NSIN物理层、数据链层、网络层和传输层的有效传输技术。最后，文章还提出了NSIN未来研究的一些开问和挑战。
Multi-Sensor Multi-Scan Radar Sensing of Multiple Extended Targets
results: 对各种高噪声场景下的多目标探测问题进行了数值实验，结果表明，提出的方法可以在高噪声场景下提供较好的性能，超过了现有的多目标跟踪算法。Abstract
We propose an efficient solution to the state estimation problem in multi-scan multi-sensor multiple extended target sensing scenarios. We first model the measurement process by a doubly inhomogeneous-generalized shot noise Cox process and then estimate the parameters using a jump Markov chain Monte Carlo sampling technique. The proposed approach scales linearly in the number of measurements and can take spatial properties of the sensors into account, herein, sensor noise covariance, detection probability, and resolution. Numerical experiments using radar measurement data suggest that the algorithm offers improvements in high clutter scenarios with closely spaced targets over state-of-the-art clustering techniques used in existing multiple extended target tracking algorithms.
摘要
我们提出一种高效的解决方案，用于多扫描多传感器多扩展目标感知场景中的状态估计问题。我们首先将测量过程模型为一种 doubly inhomogeneous-generalized shot noise Cox 过程，然后使用跳动 Markov 链 Monte Carlo 样本技术来估计参数。我们的方法与测量数据量直接相关，并能考虑探测器的空间性能，包括探测器噪声卷积矩阵、检测概率和分辨率。数值实验使用雷达测量数据表明，我们的算法在高噪场景中，具有较高的性能，超过现有多扩展目标跟踪算法中的聚类技术。
A unified framework for STAR-RIS coefficients optimization
results: 对下行传输的总比特率最大化问题进行了示例应用，并获得了比其他已有算法更好的性能。此外，研究还发现，使用4或2个离散阶跃STAR-RIS可以达到相当于连续阶跃的性能水平，这是首次发现离散阶跃不一定导致显著性能下降。Abstract
Simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS), which serves users located on both sides of the surface, has recently emerged as a promising enhancement to the traditional reflective only RIS. Due to the lack of a unified comparison of communication systems equipped with different modes of STAR-RIS and the performance degradation caused by the constraints involving discrete selection, this paper proposes a unified optimization framework for handling the STAR-RIS operating mode and discrete phase constraints. With a judiciously introduced penalty term, this framework transforms the original problem into two iterative subproblems, with one containing the selection-type constraints, and the other subproblem handling other wireless resource. Convergent point of the whole algorithm is found to be at least a stationary point under mild conditions. As an illustrative example, the proposed framework is applied to a sum-rate maximization problem in the downlink transmission. Simulation results show that the algorithms from the proposed framework outperform other existing algorithms tailored for different STAR-RIS scenarios. Furthermore, it is found that 4 or even 2 discrete phases STAR-RIS could achieve almost the same sum-rate performance as the continuous phase setting, showing for the first time that discrete phase is not necessarily a cause of significant performance degradation.
摘要
同时传输和反射（STAR-RIS）可重配置智能表面，该表面服务于两侧用户，近期崛起为传统反射only RIS的增强。由于不同 communicate systems 装备不同模式的 STAR-RIS 的比较缺乏统一的评估，这篇论文提出了一个统一优化框架，用于处理 STAR-RIS 运行模式和离散阶段约束。通过在搜索过程中引入罚项，该框架将原始问题转化为两个迭代子问题，其中一个包含选择类约束，另一个子问题处理其他无线资源。对整个算法的转移点，存在轻度条件下的 converges 点。在应用于下链传输的吞吐量最大化问题中，使用提出的框架的算法比其他适用于不同 STAR-RIS 场景的算法更高效。此外，发现4或两个离散阶段 STAR-RIS 可以达到相当于连续阶段的吞吐量性能，这是第一次发现离散阶段不一定会导致显著性能下降。
A Two-Stage 2D Channel Extrapolation Scheme for TDD 5G NR Systems
results: 对比基elines，提议的通道投影方案在实验结果中表现出优于基elines，能够更好地捕捉大量MIMO-OFDM通道的动态稠密特征Abstract
Recently, channel extrapolation has been widely investigated in frequency division duplex (FDD) massive MIMO systems. However, in time division duplex (TDD) fifth generation (5G) new radio (NR) systems, the channel extrapolation problem also arises due to the hopping uplink pilot pattern, which has not been fully researched yet. This paper addresses this gap by formulating a channel extrapolation problem in TDD massive MIMO-OFDM systems for 5G NR, incorporating imperfection factors. A novel two-stage two-dimensional (2D) channel extrapolation scheme in both frequency and time domain is proposed, designed to mitigate the negative effects of imperfection factors and ensure high-accuracy channel estimation. Specifically, in the channel estimation stage, we propose a novel multi-band and multi-timeslot based high-resolution parameter estimation algorithm to achieve 2D channel extrapolation in the presence of imperfection factors. Then, to avoid repeated multi-timeslot based channel estimation, a channel tracking stage is designed during the subsequent time instants, in which a sparse Markov channel model is formulated to capture the dynamic sparsity of massive MIMO-OFDM channels under the influence of imperfection factors. Next, an expectation-maximization (EM) based compressive channel tracking algorithm is designed to jointly estimate unknown imperfection and channel parameters by exploiting the high-resolution prior information of the delay/angle parameters from the previous timeslots. Simulation results underscore the superior performance of our proposed channel extrapolation scheme over baselines.
摘要
近些年，频分多路多Input Multiple Output（MIMO）系统中的通道拓展问题得到了广泛的研究。然而，在时分多路新Radio（NR）5G系统中，通道拓展问题也出现，它和各种不完美因素相关。这篇论文填补了这一漏洞，并提出了一种基于OFDM的2D通道拓展方案，以减轻不完美因素的负面影响，并确保高精度通道估计。具体来说，在通道估计阶段，我们提出了一种基于多频和多时槽的高分辨率参数估计算法，以实现2D通道拓展在存在不完美因素的情况下。然后，为了避免重复的多时槽基本通道估计，我们设计了一个通道跟踪阶段，在接下来的时间点instants中，采用了一个简单的Markov通道模型来捕捉大量MIMO-OFDM通道的动态稀热性。接着，我们设计了一个基于极大似然估计的压缩通道跟踪算法，以同时估计不完美因素和通道参数。实验结果表明，我们的提出的通道拓展方案在基准下表现出优于性。
Spiking Semantic Communication for Feature Transmission with HARQ
paper_authors: Mengyang Wang, Jiahui Li, Mengyao Ma, Xiaopeng Fan
for: This paper aims to improve the performance of Semantic Communication (SC) models in Collaborative Intelligence (CI) systems by introducing a novel SC model called SNN-SC-HARQ, which combines SNN-based SC models with the Hybrid Automatic Repeat Request (HARQ) mechanism.
methods: The proposed SNN-SC-HARQ model uses a combination of SNN-based SC models and a policy model to dynamically adjust the transmission bandwidth based on channel conditions, without sacrificing performance.
results: Experimental results show that SNN-SC-HARQ can dynamically adjust the bandwidth according to the channel conditions without performance loss, improving the overall performance of SC models in CI systems.Abstract
In Collaborative Intelligence (CI), the Artificial Intelligence (AI) model is divided between the edge and the cloud, with intermediate features being sent from the edge to the cloud for inference. Several deep learning-based Semantic Communication (SC) models have been proposed to reduce feature transmission overhead and mitigate channel noise interference. Previous research has demonstrated that Spiking Neural Network (SNN)-based SC models exhibit greater robustness on digital channels compared to Deep Neural Network (DNN)-based SC models. However, the existing SNN-based SC models require fixed time steps, resulting in fixed transmission bandwidths that cannot be adaptively adjusted based on channel conditions. To address this issue, this paper introduces a novel SC model called SNN-SC-HARQ, which combines the SNN-based SC model with the Hybrid Automatic Repeat Request (HARQ) mechanism. SNN-SC-HARQ comprises an SNN-based SC model that supports the transmission of features at varying bandwidths, along with a policy model that determines the appropriate bandwidth. Experimental results show that SNN-SC-HARQ can dynamically adjust the bandwidth according to the channel conditions without performance loss.
摘要
在合作智能（CI）中，人工智能（AI）模型被分为边缘和云端两部分，中间特征从边缘传输到云端进行推理。一些基于深度学习的语意通信（SC）模型已经被提出，以减少特征传输开销和抵消通道干扰。前一些研究表明，使用快速 нейрон网络（SNN）基于的 SC 模型在数字通道上比使用深度神经网络（DNN）基于的 SC 模型更加强健。然而，现有的 SNN 基于的 SC 模型具有固定时间步，导致固定的传输宽度，无法根据通道条件进行适应调整。为解决这个问题，本文提出了一种新的 SC 模型，即 SNN-SC-HARQ，它结合了 SNN 基于的 SC 模型和混合自动重传请求（HARQ）机制。SNN-SC-HARQ 包括一个基于 SNN 的 SC 模型，可以在不同宽度下传输特征，以及一个策略模型，用于确定适当的宽度。实验结果表明，SNN-SC-HARQ 可以根据通道条件动态调整宽度，无需 sacrificing performance。
Quickest Change Detection in Autoregressive Models
results: 提出了一种数据驱动的 online和 computationally efficient gradient ascent CuSum算法，可以在false alarm控制下检测到变化。同时，还 deriv了lower bound on its average running length to false alarm。 simulate results demonstrate the performance of the proposed algorithms.Abstract
The problem of quickest change detection (QCD) in autoregressive (AR) models is investigated. A system is being monitored with sequentially observed samples. At some unknown time, a disturbance signal occurs and changes the distribution of the observations. The disturbance signal follows an AR model, which is dependent over time. Before the change, observations only consist of measurement noise, and are independent and identically distributed (i.i.d.). After the change, observations consist of the disturbance signal and the measurement noise, are dependent over time, which essentially follow a continuous-state hidden Markov model (HMM). The goal is to design a stopping time to detect the disturbance signal as quickly as possible subject to false alarm constraints. Existing approaches for general non-i.i.d. settings and discrete-state HMMs cannot be applied due to their high computational complexity and memory consumption, and they usually assume some asymptotic stability condition. In this paper, the asymptotic stability condition is firstly theoretically proved for the AR model by a novel design of forward variable and auxiliary Markov chain. A computationally efficient Ergodic CuSum algorithm that can be updated recursively is then constructed and is further shown to be asymptotically optimal. The data-driven setting where the disturbance signal parameters are unknown is further investigated, and an online and computationally efficient gradient ascent CuSum algorithm is designed. The algorithm is constructed by iteratively updating the estimate of the unknown parameters based on the maximum likelihood principle and the gradient ascent approach. The lower bound on its average running length to false alarm is also derived for practical false alarm control. Simulation results are provided to demonstrate the performance of the proposed algorithms.
摘要
文本中的快速变化检测（QCD）问题在某些杂音模型（AR）中进行了研究。系统通过连续观测样本进行监测，并且在未知时间点上发生了干扰信号，该干扰信号随时间的变化而改变观测值的分布。在干扰之前，观测值只包含测量噪声，而测量噪声是独立同分布的。在干扰后，观测值包含干扰信号和测量噪声，这些观测值随时间的变化而成为连续状态隐марков链（HMM）。检测干扰信号的目标是在最短时间内检测干扰信号，并且具有False Alarm控制的限制。现有的方法无法应用于这种非i.i.d.设置和离散状态HMM，因为它们具有高的计算复杂性和内存占用。此外，这些方法通常假设某种 asymptotic stability condition。本文首次 теоретиче上证明了AR模型的 asymptotic stability condition，并构建了一种可更新的Ergodic CuSum算法。此外，一种在线和计算效率高的梯度上升CuSum算法也被构建，该算法基于最大有希望似然原理和梯度上升方法来更新未知参数的估计。此外，对于实际false alarm控制，我们还 deriv了lower bound的平均跑道时间。实验结果显示了提案的算法的性能。