methods: 我们提出了两种活动预测方法，即基于深度学习（DL）和 Kriging interpolación。第一种方法不依赖发送器位置，并利用3D地图补做不精确的预测。我们使用DL来 incorporate 3D maps into prediction和 reinforcement learning for optimal path planning for UAVs based on DL prediction。第二种方法基于 Kriging interpolación，需要知道发送器位置，而且不能使用3D地图。我们在一个基于射线追踪的通道模拟器中训练和评估两种提议的方法。
results: 我们通过 simulated experiments demonstrate the importance of active prediction compared to prediction based on randomly collected measurements of channel gain。另外，我们还表明使用 DL 和 3D maps，可以在不知道发送器位置的情况下实现高精度预测。 finally，我们还证明了在使用多个 UAVs 采集测量时，协调的路径规划对于活动预测具有重要的重要性。Abstract
Prediction of wireless channel gain (CG) across space is a necessary tool for many important wireless network design problems. In this paper, we develop prediction methods that use environment-specific features, namely building maps and CG measurements, to achieve high prediction accuracy. We assume that measurements are collected using a swarm of coordinated unmanned aerial vehicles (UAVs). We develop novel active prediction approaches which consist of both methods for UAV path planning for optimal measurement collection and methods for prediction of CG across space based on the collected measurements. We propose two active prediction approaches based on deep learning (DL) and Kriging interpolation. The first approach does not rely on the location of the transmitter and utilizes 3D maps to compensate for the lack of it. We utilize DL to incorporate 3D maps into prediction and reinforcement learning for optimal path planning for the UAVs based on DL prediction. The second active prediction approach is based on Kriging interpolation, which requires known transmitter location and cannot utilize 3D maps. We train and evaluate the two proposed approaches in a ray-tracing-based channel simulator. Using simulations, we demonstrate the importance of active prediction compared to prediction based on randomly collected measurements of channel gain. Furthermore, we show that using DL and 3D maps, we can achieve high prediction accuracy even without knowing the transmitter location. We also demonstrate the importance of coordinated path planning for active prediction when using multiple UAVs compared to UAVs collecting measurements independently in a greedy manner.
摘要
<>按照以下准则进行简化中文翻译：1. 使用标准中文翻译词汇和 grammar2. 尽可能简化语句结构和表达3. 保留原文的意思和主题Prediction of wireless channel gain (CG) across space is a crucial tool for many important wireless network design problems. In this paper, we develop prediction methods that use environment-specific features, namely building maps and CG measurements, to achieve high prediction accuracy. We assume that measurements are collected using a swarm of coordinated unmanned aerial vehicles (UAVs). We develop novel active prediction approaches that consist of both methods for UAV path planning for optimal measurement collection and methods for prediction of CG across space based on the collected measurements. We propose two active prediction approaches based on deep learning (DL) and Kriging interpolation. The first approach does not rely on the location of the transmitter and utilizes 3D maps to compensate for the lack of it. We utilize DL to incorporate 3D maps into prediction and reinforcement learning for optimal path planning for the UAVs based on DL prediction. The second active prediction approach is based on Kriging interpolation, which requires known transmitter location and cannot utilize 3D maps. We train and evaluate the two proposed approaches in a ray-tracing-based channel simulator. Using simulations, we demonstrate the importance of active prediction compared to prediction based on randomly collected measurements of channel gain. Furthermore, we show that using DL and 3D maps, we can achieve high prediction accuracy even without knowing the transmitter location. We also demonstrate the importance of coordinated path planning for active prediction when using multiple UAVs compared to UAVs collecting measurements independently in a greedy manner.Translated text:预测无线通道增强（CG）在空间是许多重要无线网络设计问题中的必需工具。在这篇论文中，我们开发了预测方法，使用环境特定特征，namely building maps和CG测量，以实现高预测精度。我们假设测量是通过一群协调的无人飞行器（UAVs）进行收集。我们开发了两种活动预测方法，它们分别基于深度学习（DL）和 Kriging interpolate。第一种方法不依赖发送器位置，并利用3D地图补偿发送器位置的缺失。我们利用 DL 将3D地图 incorporated into prediction，并通过强化学习对 UAVs 的路径规划进行优化，基于 DL 预测。第二种方法基于 Kriging interpolate，它需要知道发送器位置，而且无法使用3D地图。我们在一个基于投影法的通道模拟器中训练和评估了两种提议的方法。使用仿真，我们表明了活动预测比随机收集通道增强的预测更重要。此外，我们还表明了使用 DL 和3D地图，我们可以在不知道发送器位置的情况下实现高预测精度。我们还 demonstarted 多个 UAVs 协调的路径规划对活动预测的重要性。
Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies
paper_authors: Olfa Ben Yahia, Zineb Garroussi, Olivier Bélanger, Brunilde Sansò, Jean-François Frigon, Stéphane Martel, Antoine Lesage-Landry, Gunes Karabulut Kurt
For: The paper provides a comprehensive state-of-the-art of high throughput satellite (HTS) systems and envisions the next generation of extremely high-throughput satellite (EHTS) systems.* Methods: The paper discusses various techniques such as beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas that are being used to improve the performance of HTS systems.* Results: The paper provides a vision for future EHTS systems that will maximize spectrum reuse and data rates, and flexibly steer capacity to satisfy user demand. Additionally, the paper introduces a novel architecture for future regenerative payloads and summarizes the challenges imposed by this architecture.Abstract
High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G networks. HTS are mainly designed to provide higher data rates and capacities. Fueled by technological advancements including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas, HTS have emerged as a fundamental component for future network generation. This paper offers a comprehensive state-of-the-art of HTS systems, with a focus on standardization, patents, channel multiple access techniques, routing, load balancing, and the role of software-defined networking (SDN). In addition, we provide a vision for next-satellite systems that we named as extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed such that it maximizes spectrum reuse and data rates, and flexibly steers the capacity to satisfy user demand. We introduce a novel architecture for future regenerative payloads while summarizing the challenges imposed by this architecture.
摘要
高通信率卫星（HTS）预计将扮演6G网络的关键激活器。HTS主要用于提供更高的数据速率和容量。驱动技术的进步，包括射频扫描、高级调制技术、可编程相位阵列技术和电子扫描天线，使HTS成为未来网络代表性的组件。本文提供了HTS系统的全面状态艺术，强调标准化、套件、通道多访问技术、路由、负荷均衡和软件定义网络（SDN）的角色。此外，我们还提出了下一代卫星系统，我们称之为“极高通信率卫星”（EHTS），该系统将具备自主卫星的主要需求和关键技术。EHTS系统将实现spectrum reuse和数据速率的最大化，并可以自动调整容量来满足用户需求。我们还介绍了未来复合 payload 的新架构，并总结了这种架构带来的挑战。
Enhanced Backpressure Routing Using Wireless Link Features
methods: 使用 Biased BP 和短路寻址机制，不增加每次时间步骤的信号量 overhead
results: 提出了优化积分偏好、保持偏好在移动环境下、以及 incorporating sojourn time awareness into biased BP 等三个长期挑战，并通过分析和实验证明其效果。Abstract
Backpressure (BP) routing is a well-established framework for distributed routing and scheduling in wireless multi-hop networks. However, the basic BP scheme suffers from poor end-to-end delay due to the drawbacks of slow startup, random walk, and the last packet problem. Biased BP with shortest path awareness can address the first two drawbacks, and sojourn time-based backlog metrics were proposed for the last packet problem. Furthermore, these BP variations require no additional signaling overhead in each time step compared to the basic BP. In this work, we further address three long-standing challenges associated with the aforementioned low-cost BP variations, including optimal scaling of the biases, bias maintenance under mobility, and incorporating sojourn time awareness into biased BP. Our analysis and experimental results show that proper scaling of biases can be achieved with the help of common link features, which can effectively reduce end-to-end delay of BP by mitigating the random walk of packets under low-to-medium traffic, including the last packet scenario. In addition, our low-overhead bias maintenance scheme is shown to be effective under mobility, and our bio-inspired sojourn time-aware backlog metric is demonstrated to be more efficient and effective for the last packet problem than existing approaches when incorporated into biased BP.
摘要
背压路由（BP）是无线多项网络中分布路由和排程的一个成熟框架。然而，基本BP方案受到终端到终端延迟的问题，包括启动时间较慢、随机漫步和最后一个包问题。偏好BP可以解决首两个问题，而且可以使用游历时间-基础的伙伴度量来解决最后一个包问题。此外，这些BP变化不需要每个时间步骤中额外的讯号过程。在这个工作中，我们进一步解决了这些低成本BP变化的三个长期挑战，包括对偏好的优化维护、在移动环境中维护偏好以及将游历时间意识到偏好BP中。我们的分析和实验结果显示，正确地对偏好进行缩小可以使用通用链接特征来减少BP对终端的延迟，包括最后一个包enario。此外，我们的低负载维护方案在移动环境中是有效的，并且将游历时间意识到偏好BP中的方法比较高效和有效。
A physics-informed generative model for passive radio-frequency sensing
methods: 使用physics-informed生成神经网络 (GNN) 模型，将电磁波体diffraction方法 incorporated into variational autoencoder (VAE) 技术，以便模拟/重建缺失的样本或学习受物理法则约束的数据分布。
results: 与传统diffraction-based EM body工具相比，提出的 EM-informed生成模型能够更好地预测真实的电磁波强度，并且在实际测量数据上验证了其有效性。Abstract
Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) stray radiation received by wireless devices nearby. These wireless devices may be co-located members of a Wireless Local Area Network (WLAN) or even cellular devices connected with a Wide Area Network (WAN). Despite their accuracy, EM models are time-consuming methods which prevent their adoption in strict real-time computational imaging problems and Bayesian estimation, such as passive localization, RF tomography, and holography. Physics-informed Generative Neural Network (GNN) models have recently attracted a lot of attention thanks to their potential to reproduce a process by incorporating relevant physical laws and constraints. Thus, GNNs can be used to simulate/reconstruct missing samples, or learn physics-informed data distributions. The paper discusses a Variational Auto-Encoder (VAE) technique and its adaptations to incorporate a relevant EM body diffraction method with applications to passive RF sensing and localization/tracking. The proposed EM-informed generative model is verified against classical diffraction-based EM body tools and validated on real RF measurements. Applications are also introduced and discussed.
摘要
电磁体（EM）模型预测人员存在和运动对附近无线设备接收的 радио频偏振（RF）杂谱的影响。这些无线设备可能是分布在同一个地方的无线本地网络（WLAN）成员或者连接到宽带网络（WAN）的无线设备。尽管它们的准确性很高，但EM模型是时间consuming的方法，这阻碍了它们在严格的实时计算图像问题和 bayesian估计中的采用。物理学 Informed Generative Neural Network（GNN）模型在最近吸引了很多关注，因为它们可以通过包含相关的物理法律和约束来重现一个过程。因此，GNN可以用来 simulate/重construct缺失的样本，或者学习物理学 Informed 数据分布。文章介绍了一种 Variational Auto-Encoder（VAE）技术和其修改，以包含相关的EM体 diffraction 方法，并应用于无线RF感知和定位/跟踪。提出的EM-informed生成模型被证明了 классиical diffraction-based EM体工具和实际RF测量。应用也是介绍和讨论的。
Physics-assisted machine learning for THz spectroscopy: sensing moisture on plant leaves
results: 研究人员通过对 12,000 个水pattern 的 THz 时间域数据进行分析，提出了关于决定水Pattern 的重要发现，并证明了这些模型在不同的测试集上的普适性。Abstract
Signal processing techniques are of vital importance to bring THz spectroscopy to a maturity level to reach practical applications. In this work, we illustrate the use of machine learning techniques for THz time-domain spectroscopy assisted by domain knowledge based on light-matter interactions. We aim at the potential agriculture application to determine the amount of free water on plant leaves, so-called leaf wetness. This quantity is important for understanding and predicting plant diseases that need leaf wetness for disease development. The overall transmission of a moist plant leaf for 12,000 distinct water patterns was experimentally acquired using THz time-domain spectroscopy. We report on key insights of applying decision trees and convolutional neural networks to the data using physics-motivated choices. Eventually, we discuss the generalizability of these models to determine leaf wetness after testing them on cases with increasing deviations from the training set.
摘要
<>translate "Signal processing techniques are of vital importance to bring THz spectroscopy to a maturity level to reach practical applications. In this work, we illustrate the use of machine learning techniques for THz time-domain spectroscopy assisted by domain knowledge based on light-matter interactions. We aim at the potential agriculture application to determine the amount of free water on plant leaves, so-called leaf wetness. This quantity is important for understanding and predicting plant diseases that need leaf wetness for disease development. The overall transmission of a moist plant leaf for 12,000 distinct water patterns was experimentally acquired using THz time-domain spectroscopy. We report on key insights of applying decision trees and convolutional neural networks to the data using physics-motivated choices. Eventually, we discuss the generalizability of these models to determine leaf wetness after testing them on cases with increasing deviations from the training set." into Simplified Chinese.中文简体版：信号处理技术对于 THz спектроскопия的成熔度具有核心重要性，以实现实用应用。本工作介绍了基于光物理相互作用的机器学习技术在 THz 时域спектроскопии中的应用。我们target了农业应用，通过测量植物叶子上的自由水量，也称为叶质湿度。这个量对于理解和预测植物疾病非常重要，疾病发展需要叶质湿度。我们通过 THz 时域спектроскопии实验获得了12,000个不同水平的叶质湿度数据。我们使用决策树和卷积神经网络对数据进行分析，并根据物理原理进行选择。最后，我们讨论了这些模型在不同于训练集的情况下的泛化性。