results: 作者验证了提议的技术，并发现了需要进一步研究设计有效的频率需求或频率使用预测信号的方法。Abstract
The coexistence between active wireless communications and passive RF spectrum use becomes an increasingly important requirement for coordinated spectrum access supporting critical services. The ongoing research and technological progress are focused on effective spectrum utilization including large-scale MIMO and energy efficient and low-power communications, innovative spectrum use and management, and resilient spectrum sharing, just to name a few. This paper introduces a new tool for real time spectrum sharing among emerging cellular networks and passive RF sensing systems used for remote sensing and radio astronomy, among others. Specifically we propose leveraging wireless channel virtualization and propose a virtual-to-physical resource mapping framework, mapping types, and control signaling that extends the current 5G New Radio (NR) specifications. Our technology introduces minimal changes to the protocol and is meant to be transparent to the end user application. We validate the proposed technology by extending a 3GPP compliant 5G NR downlink simulator and identify further research directions where work is needed on designing effective ways to explicitly signal the need for spectrum or spectrum use predictions.
摘要
“ aktive wireless 通信和温馈 RF 频率使用的共存变得越来越重要，以支持协调的频率访问，以满足重要服务的需求。当前的研究和技术进步都在注重有效的频率利用，包括大规模 MIMO 和能效的低功率通信，创新的频率使用和管理，以及可靠的频率分享。这篇文章介绍了一种新的实时频率分享工具，用于将趋起的Cellular网络和温馈 RF 感知系统连接起来，用于远程感知和天文学等。我们建议利用无线通道虚拟化，并提出了虚拟资源映射框架，类型和控制信号。我们的技术对协议做出了最小的改变，并且透明于应用程序端。我们验证了我们的技术，并确定了进一步的研究方向，包括设计有效的频率需求或频率使用预测的方法。”
Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device
results: 本研究的结果表明，使用TinyML框架可以实现轻量级深度学习模型，并且可以在不同温度（0℃-90℃）下进行损害检测，并且可以实现边缘推理。Abstract
Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units (GPUs), various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages. Such models typically comprise of millions of trainable parameters, and therefore add to cost of deployment due to requirements of cloud connectivity and processing, thus limiting the scale of deployment of GW-SHM. In this work, we propose an alternative solution that leverages TinyML framework for development of light-weight ML models that could be directly deployed on embedded edge devices. The utility of our solution is illustrated by presenting an unsupervised learning framework for damage detection in honeycomb composite sandwich structure (HCSS) with disbond and delamination type of damages, validated using data generated by finite element (FE) simulations and experiments performed at various temperatures in the range 0{\deg}C to 90{\deg}C. We demonstrate a fully-integrated solution using a Xilinx Artix-7 FPGA for data acquisition and control, and edge-inference of damage.
摘要
驱动了机器学习（ML）的快速发展以及更多的云计算和图像处理器（GPU）的访问，各种深度学习基于模型已经被提议用于改善ultrasound guided wave结构Integrity monitoring systems（GW-SHM）的性能，特别是面临 complexity和不同环境因素（例如温度）以及不同类型的损害。这些模型通常包含数百万可训练参数，因此增加了部署成本，因为需要云连接和处理，从而限制了GW-SHM的规模。在这种工作中，我们提出了一个替代解决方案，利用TinyML框架 для开发轻量级ML模型，可以直接在嵌入式边缘设备上部署。我们的解决方案的有用性被示例了，通过对受损 Composite sandwich structure（HCSS）中的损害探测进行无监督学习框架，验证了在0℃至90℃的温度范围内进行了数据生成的Finite element（FE） simulations和实验。我们还示出了一个完全 интеGRATED解决方案，使用Xilinx Artix-7 FPGA进行数据收集和控制，以及边缘推理损害。
Secure Short-Packet Transmission with Aerial Relaying: Blocklength and Trajectory Co-Design
results: 提出一种低复杂度算法，通过分解原问题为两个互相独立的子问题，以循环迭代法解决，实现优化性能。实验结果显示，提出的设计方案在其他参考方案相比，具有显著的性能改进。Abstract
In this paper, we propose a secure short-packet communication (SPC) system involving an unmanned aerial vehicle (UAV)-aided relay in the presence of a terrestrial passive eavesdropper. The considered system, which is applicable to various next-generation Internet-of-Things (IoT) networks, exploits a UAV as a mobile relay, facilitating the reliable and secure exchange of intermittent short packets between a pair of remote IoT devices with strict latency. Our objective is to improve the overall secrecy throughput performance of the system by carefully designing key parameters such as the coding blocklengths and the UAV trajectory. However, this inherently poses a challenging optimization problem that is difficult to solve optimally. To address the issue, we propose a low-complexity algorithm inspired by the block successive convex approximation approach, where we divide the original problem into two subproblems and solve them alternately until convergence. Numerical results demonstrate that the proposed design achieves significant performance improvements relative to other benchmarks, and offer valuable insights into determining appropriate coding blocklengths and UAV trajectory.
摘要
在本文中，我们提出了一种安全短包通信（SPC）系统，其中一架不可持续飞行器（UAV）作为中继器，在地面通信过悬挂的听写器存在下进行通信。这种系统适用于多种下一代互联网关系（IoT）网络，通过UAV作为移动中继器，实现了两个远程IoT设备之间的可靠和安全短包交换，并且具有严格的延迟要求。我们的目标是通过精心设计密钥参数，如编码块长度和UAV轨迹，提高整体密钥传输性能。但这会导致一个困难的优化问题，这个问题难以得到最优解。为了解决这个问题，我们提出了一种低复杂度算法，基于短SUCA（successive convex approximation）方法，将原始问题分解成两个互补问题，并在它们之间 alternate 到达循环至收敛。实验结果表明，提案的设计在其他参考模型的基础上具有显著的性能改进，并且为确定合适的编码块长度和UAV轨迹提供了有价值的洞察。
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization
results: 通过分析和实验研究，发现通信错误和混合矩阵的spectral gap对学习性能有显著影响，并提出了一种joint通信学习优化问题来优化发射器扬理和混合矩阵。Abstract
Decentralized federated learning (DFL), inherited from distributed optimization, is an emerging paradigm to leverage the explosively growing data from wireless devices in a fully distributed manner.DFL enables joint training of machine learning model under device to device (D2D) communication fashion without the coordination of a parameter server. However, the deployment of wireless DFL is facing some pivotal challenges. Communication is a critical bottleneck due to the required extensive message exchange between neighbor devices to share the learned model. Besides, consensus becomes increasingly difficult as the number of devices grows because there is no available central server to perform coordination. To overcome these difficulties, this paper proposes employing over-the-air computation (Aircomp) to improve communication efficiency by exploiting the superposition property of analog waveform in multi-access channels, and introduce the mixing matrix mechanism to promote consensus using the spectral property of symmetric doubly stochastic matrix. Specifically, we develop a novel multiple-input multiple-output over-the-air DFL (MIMO OA-DFL) framework to study over-the-air DFL problem over MIMO multiple access channels. We conduct a general convergence analysis to quantitatively capture the influence of aggregation weight and communication error on the MIMO OA-DFL performance in \emph{ad hoc} networks. The result shows that the communication error together with the spectral gap of mixing matrix has a significant impact on the learning performance. Based on this, a joint communication-learning optimization problem is formulated to optimize transceiver beamformers and mixing matrix. Extensive numerical experiments are performed to reveal the characteristics of different topologies and demonstrate the substantial learning performance enhancement of our proposed algorithm.
摘要
《分布式聚合学习（DFL）》是一种利用无线设备上爆发式增长的数据进行完全分布式的学习 paradigm。DFL可以在设备之间（D2D）进行共同训练机器学习模型，而无需协调参数服务器。然而，无线DFL的部署受到一些重要挑战。通信是一个关键的瓶颈，因为需要在邻居设备之间进行广泛的消息交换，以便分享学习模型。此外，在设备数量增加时，协调变得越来越困难，因为没有可用的中央服务器进行协调。为了解决这些困难，本文提出了使用空中计算（Aircomp）来提高通信效率，并利用多元Access通道上的超позиция性来实现。此外，我们还引入了混合矩阵机制来促进协调，使用幂等矩阵的特性来提高协调性。我们开发了一种多输入多输出（MIMO）无线DFL框架，以研究无线DFL问题在MIMO多接收通道上。我们进行了一种通用的叠加分析，以量化协调积分和通信错误对MIMO OA-DFL性能的影响。结果显示，通信错误以及混合矩阵的spectral gap具有显著影响学习性能。基于这些结果，我们提出了一个共同通信学习优化问题，以优化天线扬发器和混合矩阵。我们进行了广泛的数学实验，以描述不同的Topology特性和 demonstate MIMO OA-DFL性能的明显提高。
Performance Analysis of RIS-Aided Double Spatial Scattering Modulation for mmWave MIMO Systems
results: 通过两种不同的方法，我们 derivated了准确的数学积分和闭合式表达式，以及Upper bound和 asymptotic表达式。此外，我们还给出了RIS-DSSM方案的多样性 gain。进一步，我们通过组合UPEP和错误位数来获得了Union upper bound of average bit error probability（ABEP）。在实验中，我们验证了所 derivated的Upper bound和asymptotic表达式。发现在提posed system-based phase shift keying（PSK）中，ABEP性能比quadrature amplitude modulation（QAM）好。此外，随着RIS元素的增加，ABEP性能的提高更加明显。Abstract
In this paper, we investigate a practical structure of reconfigurable intelligent surface (RIS)-based double spatial scattering modulation (DSSM) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. A suboptimal detector is proposed, in which the beam direction is first demodulated according to the received beam strength, and then the remaining information is demodulated by adopting the maximum likelihood algorithm. Based on the proposed suboptimal detector, we derive the conditional pairwise error probability expression. Further, the exact numerical integral and closed-form expressions of unconditional pairwise error probability (UPEP) are derived via two different approaches. To provide more insights, we derive the upper bound and asymptotic expressions of UPEP. In addition, the diversity gain of the RIS-DSSM scheme was also given. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained by combining the UPEP and the number of error bits. Simulation results are provided to validate the derived upper bound and asymptotic expressions of ABEP. We found an interesting phenomenon that the ABEP performance of the proposed system-based phase shift keying is better than that of the quadrature amplitude modulation. Additionally, the performance advantage of ABEP is more significant with the increase in the number of RIS elements.
摘要
在这篇论文中，我们研究了一种实用的可重新配置智能表面（RIS）基于Double Spatial Scattering Modulation（DSSM）的 millimeter 波（mmWave）多输入多出力（MIMO）系统。我们提出了一种不优的探测器，其中首先根据接收到的磁场强度将磁场方向进行模式化，然后采用最大 LIKElihood算法进行模式化。基于我们提出的不优探测器，我们 deriv了Conditional Pairwise Error Probability表达式。进一步，我们通过两种不同的方法 deriv了无条件 Pairwise Error Probability（UPEP）的精确数学 интеграル和闭合式表达式。为了提供更多的意见，我们 deriv了UPEP的上界和极限表达式。此外，我们还给出了RIS-DSSM方案的多样性收益。此外，我们通过将UPEP和错误比特数相加来获得了Union Upper Bound of Average Bit Error Probability（ABEP）的Upper bound。我们通过实验 validate了我们 deriv的Upper bound和极限表达式。我们发现了一种有趣的现象，即提posed systembased phase shift keying的ABEP性能比quadrature amplitude modulation更好。此外，ABEP性能的提升程度随RIS元素的增加而更加显著。
Robust matrix completion via Novel M-estimator Functions
methods: generates a class of nonconvex functions to down-weight outlier-corrupted observations, and develops efficient algorithms based on these functions
results: superior recovery accuracy and runtime compared to competitorsAbstract
M-estmators including the Welsch and Cauchy have been widely adopted for robustness against outliers, but they also down-weigh the uncontaminated data. To address this issue, we devise a framework to generate a class of nonconvex functions which only down-weigh outlier-corrupted observations. Our framework is then applied to the Welsch, Cauchy and $\ell_p$-norm functions to produce the corresponding robust loss functions. Targeting on the application of robust matrix completion, efficient algorithms based on these functions are developed and their convergence is analyzed. Finally, extensive numerical results demonstrate that the proposed methods are superior to the competitors in terms of recovery accuracy and runtime.
摘要
M-estimators including Welsch 和 Cauchy 已经广泛采用，但它们也会减小不受污染的数据。为解决这个问题，我们提出了一个框架，生成一类非凸函数，只有在受污染观测值时减小。我们然后应用这个框架到 Welsch、Cauchy 和 $\ell_p$-norm 函数，生成相应的Robust loss函数。我们然后开发了高效的算法，并分析它们的收敛性。最后，我们在大量的数据上进行了丰富的数据测试，并证明了我们的方法在准确率和运行时间上都高于竞争对手。Note: "M-estimators" is translated as "M-估计器" in Simplified Chinese, and "robust loss functions" is translated as "Robust 损失函数".