methods: 提出一种新的flexible aggregation-based FL（F$^2$L）模型，使用orthogonal frequency division multiple-access（OFDMA）空 Interface，让选择的客户端在每个聚合轮次中训练本地模型，并在聚合轮次之前进行多次迭代。选择客户端、子频和模ulation，根据通道条件和计算能力进行适应。
results: 通过分析OFDMA-F$^2$L的最佳性 gap，并使用Lagrange-dual方法解决权衡积sum rate最大化的挑战性混合整数程序，发现使用“赢家当选”策略可以实现最佳客户端、子频和模ulation选择。实验中，OFDMA-F$^2$L可以提高训练的速度和精度，比如18%和5%，相比潜在的替代方案。Abstract
Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence. The impact of practical air interfaces with discrete modulations on FL has not previously been studied in depth. This paper proposes a new paradigm of flexible aggregation-based FL (F$^2$L) over orthogonal frequency division multiple-access (OFDMA) air interface, termed as ``OFDMA-F$^2$L'', allowing selected clients to train local models for various numbers of iterations before uploading the models in each aggregation round. We optimize the selections of clients, subchannels and modulations, adapting to channel conditions and computing powers. Specifically, we derive an upper bound on the optimality gap of OFDMA-F$^2$L capturing the impact of the selections, and show that the upper bound is minimized by maximizing the weighted sum rate of the clients per aggregation round. A Lagrange-dual based method is developed to solve this challenging mixed integer program of weighted sum rate maximization, revealing that a ``winner-takes-all'' policy provides the almost surely optimal client, subchannel, and modulation selections. Experiments on multilayer perceptrons and convolutional neural networks show that OFDMA-F$^2$L with optimal selections can significantly improve the training convergence and accuracy, e.g., by about 18\% and 5\%, compared to potential alternatives.
摘要
“联合学习（FL）在移动网络中部署时可能会遇到通信瓶颈，限制参与的客户端和妨碍FL的收敛。这篇论文提出了一种新的柔性聚合基于联合学习（F$^2$L）的方法，称为“OFDMA-F$^2$L”，允许参与客户端在每个聚合轮次中对本地模型进行不同数量的训练，然后将模型上传到总集。我们优化客户端、子频和模ulation的选择，适应通道条件和计算能力。 Specifically, we derive an upper bound on the optimality gap of OFDMA-F$^2$L, which captures the impact of the selections, and show that the upper bound is minimized by maximizing the weighted sum rate of the clients per aggregation round. A Lagrange-dual based method is developed to solve this challenging mixed integer program of weighted sum rate maximization, revealing that a \"winner-takes-all\" policy provides the almost surely optimal client, subchannel, and modulation selections. Experiments on multilayer perceptrons and convolutional neural networks show that OFDMA-F$^2$L with optimal selections can significantly improve the training convergence and accuracy, e.g., by about 18\% and 5\%, compared to potential alternatives.”Note: The translation is in Simplified Chinese, which is one of the two standard versions of Chinese used in mainland China and Singapore.
Quickest Change Detection with Post-Change Density Estimation
results: 在满足certain smoothness conditions下，当密度估计 converge时，两种测试方法具有first-order asymptotic optimality，false alarm rate随着预测值减少到零。Abstract
The problem of quickest change detection in a sequence of independent observations is considered. The pre-change distribution is assumed to be known, while the post-change distribution is unknown. Two tests based on post-change density estimation are developed for this problem, the window-limited non-parametric generalized likelihood ratio (NGLR) CuSum test and the non-parametric window-limited adaptive (NWLA) CuSum test. Both tests do not assume any knowledge of the post-change distribution, except that the post-change density satisfies certain smoothness conditions that allows for efficient non-parametric estimation. Also, they do not require any pre-collected post-change training samples. Under certain convergence conditions on the density estimator, it is shown that both tests are first-order asymptotically optimal, as the false alarm rate goes to zero. The analysis is validated through numerical results, where both tests are compared with baseline tests that have distributional knowledge.
摘要
“我们考虑了一个独立观测序列中的快速变化探测问题。我们假设了预测变化的分布知道，而后换变分布不知道。我们提出了两种基于后换变分布估计的测试，分别是窗口限定非 Parametric 普通概率函数（NGLR）CuSum 测试和非 Parametric 窗口限定自适应（NWLA）CuSum 测试。这两种测试不知道后换变分布，只知道后换变分布满足 certain smoothness conditions，允许非 Parametric 估计。此外，它们不需要预先收集后换变training samples。在某些数据散布的假设下，我们证明了这两种测试在false alarm rate接近零时是first-order asymptotically optimal。我们通过numerical results validate our analysis， comparison with基eline tests that have distributional knowledge.”Note: Simplified Chinese is used here, as it is the most widely used variety of Chinese in mainland China. However, if you prefer Traditional Chinese, I can provide that version as well.
Multiuser Beamforming for Partially-Connected Millimeter Wave Massive MIMO
results: 对于完美 CSI 情况下，提出的方案可以减少计算复杂性，但是减少了总比特率表现的差异。对于含有误差 CSI 情况下，提出的analog-only beamformer设计方案可以有效地减少多用户干扰。Abstract
Multiuser beamforming is considered for partially-connected millimeter wave massive MIMO systems. Based on perfect channel state information (CSI), a low-complexity hybrid beamforming scheme that decouples the analog beamformer and the digital beamformer is proposed to maximize the sum-rate. The analog beamformer design is modeled as a phase alignment problem to harvest the array gain. Given the analog beamformer, the digital beamformer is designed by solving a weighted minimum mean squared error problem. Then based on imperfect CSI, an analog-only beamformer design scheme is proposed, where the design problem aims at maximizing the desired signal power on the current user and minimizing the power on the other users to mitigate the multiuser interference. The original problem is then transformed into a series of independent beam nulling subproblems, where an efficient iterative algorithm using the majorization-minimization framework is proposed to solve the subproblems. Simulation results show that, under perfect CSI, the proposed scheme achieves almost the same sum-rate performance as the existing schemes but with lower computational complexity; and under imperfect CSI, the proposed analog-only beamforming design scheme can effectively mitigate the multiuser interference.
摘要
（以下是简化中文版）在具有部分连接的毫米波巨量MIMO系统中，考虑多用户扫描。基于完美通道状态信息（CSI），提出一种低复杂度混合扫描方案，该方案将数字扫描器和分析扫描器分离开来，以最大化总Bit rate。分析扫描器的设计被视为相位匹配问题，以利用阵列效应。给定分析扫描器，则数字扫描器的设计问题是解一个质量因子最小二乘问题。然后，基于不完美CSI，提出一种分析只的扫描器设计方案，该方案的目标是 Maximize the desired signal power on the current user and minimize the power on the other users to mitigate the multiuser interference. 原始问题被转换成一系列独立的扫描nulling子问题，并提出一种高效的迭代算法使用大量化-最小化框架来解决子问题。实验结果显示，在完美CSI情况下，提出的方案可以与现有方案准确性相同，但计算复杂度较低;而在不完美CSI情况下，提出的分析只的扫描器设计方案可以有效地减少多用户干扰。
Beam Training and Tracking for Extremely Large-Scale MIMO Communications
paper_authors: Kangjian Chen, Chenhao Qi, Cheng-Xiang Wang, Geoffrey Ye Li
for: 这个论文研究了非常大规模多输入多出口通信系统中的部分连接混合结构的射频场训练和跟踪。
methods: 论文提出了两Stage hybrid-field射频场训练方案，在第一个阶段，每个子阵列独立地使用多个远场通道扫描向量来近似近场的射频场。在第二个阶段，基于数字结合器的设计，将 analog combiner的输出从第一个阶段组合起来以找到最佳的码WORD。
results: 论文提出了一种基于 stationary phase 和时空同征的 beam refinement 方案（BRPSS），并开发了一种低复杂度的近场跟踪方案，使用动态模型描述通道变化，并使用扩展 Kalman 筛选器进行跟踪。 simulate 结果验证了提出的方案的有效性。Abstract
In this paper, beam training and beam tracking are investigated for extremely large-scale multiple-input-multiple-output communication systems with partially-connected hybrid combining structures. Firstly, we propose a two-stage hybrid-field beam training scheme for both the near field and the far field. In the first stage, each subarray independently uses multiple far-field channel steering vectors to approximate near-field ones for analog combining. To find the codeword best fitting for the channel, digital combiners in the second stage are designed to combine the outputs of the analog combiners from the first stage. Then, based on the principle of stationary phase and the time-frequency duality, the expressions of subarray signals after analog combining are analytically derived and a beam refinement based on phase shifts of subarrays~(BRPSS) scheme with closed-form solutions is proposed for high-resolution channel parameter estimation. Moreover, a low-complexity near-field beam tracking scheme is developed, where the kinematic model is adopted to characterize the channel variations and the extended Kalman filter is exploited for beam tracking. Simulation results verify the effectiveness of the proposed schemes.
摘要
在本文中，我们 investigate了极大规模多输入多输出通信系统中的半连接混合结构。首先，我们提出了一种两阶段混合场 beam training 方案，其中第一阶段每个子阵列独立地使用多个远场通道方向射频 vectors 来近似近场 ones。在第二阶段，我们设计了用于将数字 combiners 的输出相乘的 analog combiners。然后，基于站静相对和时空对偶性，我们Derived the expressions of subarray signals after analog combining and proposed a beam refinement based on phase shifts of subarrays (BRPSS) scheme with closed-form solutions for high-resolution channel parameter estimation.此外，我们还提出了一种低复杂度近场 beam tracking 方案，其中采用了运动模型来描述通道变化，并利用了扩展 Kalman 筛来进行 beam tracking。实验结果证明了我们提出的方案的有效性。
Simultaneous Beam Training and Target Sensing in ISAC Systems with RIS
results: 我们提出了一种基于SBTTS和PAOE方案的位置和数组方向估计（PAOE）方案，可以对线性视野通道和非线性视野通道进行估计。通过利用扫描结果，我们计算了通道之间RIS和UT的角度 arrival和角度 departure，以实现ISAC系统的扫描平衡。实验结果证明了我们的方案的有效性。Abstract
This paper investigates an integrated sensing and communication (ISAC) system with reconfigurable intelligent surface (RIS). Our simultaneous beam training and target sensing (SBTTS) scheme enables the base station to perform beam training with the user terminals (UTs) and the RIS, and simultaneously to sense the targets. Based on our findings, the energy of the echoes from the RIS is accumulated in the angle-delay domain while that from the targets is accumulated in the Doppler-delay domain. The SBTTS scheme can distinguish the RIS from the targets with the mixed echoes from the RIS and the targets. Then we propose a positioning and array orientation estimation (PAOE) scheme for both the line-of-sight channels and the non-line-of-sight channels based on the beam training results of SBTTS by developing a low-complexity two-dimensional fast search algorithm. Based on the SBTTS and PAOE schemes, we further compute the angle-of-arrival and angle-of-departure for the channels between the RIS and the UTs by exploiting the geometry relationship to accomplish the beam alignment of the ISAC system. Simulation results verify the effectiveness of the proposed schemes.
摘要
(Simplified Chinese)这篇论文研究了一种集成感知和通信（ISAC）系统，其中包含可重新配置智能表面（RIS）。我们的同时扫描和目标探测（SBTTS）方案使得基站可以通过用户终端（UT）和RIS进行扫描，并同时探测目标。根据我们的发现，RIS中的回射强度在角度延迟频谱中归集，而目标的回射强度在Doppler延迟频谱中归集。SBTTS方案可以通过混合RIS和目标的混合强度来 отличиRIS和目标。然后，我们提议一种位姿和数组方向估计（PAOE）方案，以便对线路通信频道和非线路通信频道进行估计。基于SBTTS和PAOE方案，我们进一步计算了通信频道之间RIS和UT的角度 arrival和角度 departure，以便完成ISAC系统的束Alignment。 simulate结果证明了我们的方案的有效性。
SenseAI: Real-Time Inpainting for Electron Microscopy
results: SenseAI可以快速地进行 dictionary-based inpainting，并且可以提供live reconstructions、dictionary transfer和图像质量指标的实时图表。Abstract
Despite their proven success and broad applicability to Electron Microscopy (EM) data, joint dictionary-learning and sparse-coding based inpainting algorithms have so far remained impractical for real-time usage with an Electron Microscope. For many EM applications, the reconstruction time for a single frame is orders of magnitude longer than the data acquisition time, making it impossible to perform exclusively subsampled acquisition. This limitation has led to the development of SenseAI, a C++/CUDA library capable of extremely efficient dictionary-based inpainting. SenseAI provides N-dimensional dictionary learning, live reconstructions, dictionary transfer and visualization, as well as real-time plotting of statistics, parameters, and image quality metrics.
摘要
尽管joint字典学习和稀有编码基于填充算法在电子顾问数据中有证明的成功和广泛应用，但这些算法在实时使用电子顾问时仍然无法实现。许多电子顾问应用程序中的重建时间比数据采集时间长得多，这限制了填充的使用。这一限制导致了SenseAI的开发，这是一个基于C++/CUDA的字典学习库，可以实现EXTREMELY高效的字典基本填充。SenseAI提供了N维字典学习、实时重建、字典传输和可视化，以及实时图表化统计参数和图像质量指标。
Key Issues in Wireless Transmission for NTN-Assisted Internet of Things
results: 本文对三个关键问题进行了全面的研究和分析，并提出了一些新的方案和技术。这些方案和技术能够有效地解决非地球网络中的大量连接和精准频率信息获取问题。Abstract
Non-terrestrial networks (NTNs) have become appealing resolutions for seamless coverage in the next-generation wireless transmission, where a large number of Internet of Things (IoT) devices diversely distributed can be efficiently served. The explosively growing number of IoT devices brings a new challenge for massive connection. The long-distance wireless signal propagation in NTNs leads to severe path loss and large latency, where the accurate acquisition of channel state information (CSI) is another challenge, especially for fast-moving non-terrestrial base stations (NTBSs). Moreover, the scarcity of on-board resources of NTBSs is also a challenge for resource allocation. To this end, we investigate three key issues, where the existing schemes and emerging resolutions for these three key issues have been comprehensively presented. The first issue is to enable the massive connection by designing random access to establish the wireless link and multiple access to transmit data streams. The second issue is to accurately acquire CSI in various channel conditions by channel estimation and beam training, where orthogonal time frequency space modulation and dynamic codebooks are on focus. The third issue is to efficiently allocate the wireless resources, including power allocation, spectrum sharing, beam hopping, and beamforming. At the end of this article, some future research topics are identified.
摘要
非地面网络（NTN）已成为下一代无线传输的吸引人解决方案，其可以有效地服务多种互联网件（IoT）设备。随着互联网件设备的快速增加，批量连接成为了一大挑战。非地面基站（NTBS）的远程无线信号传播导致了严重的路径损失和大量延迟，同时精准获取通道状态信息（CSI）是另一个挑战，尤其是对于高速移动的 NTBS。此外，NTBS 的board资源的缺乏也成为了资源分配的挑战。为此，我们进行了三个关键问题的研究，即：1. 通过随机访问建立无线链接和多个数据流传输。2. 在不同的通道条件下精准获取 CSI，包括通道估计和天线训练，其中寄存器时间频率空间模ulation和动态编码库在研究中备受关注。3. 有效地分配无线资源，包括功率分配、频率分享、天线跳跃和天线相机。这篇文章结束时，我们还提出了一些未来研究的主题。
Gohberg-Semencul Estimation of Toeplitz Structured Covariance Matrices and Their Inverses
results: 实验结果显示，这种新的估计方法可以对具有对角线结构的协变矩阵和其逆进行高效地估计，并且可以确保估计结果的正定definiteness。Abstract
When only few data samples are accessible, utilizing structural prior knowledge is essential for estimating covariance matrices and their inverses. One prominent example is knowing the covariance matrix to be Toeplitz structured, which occurs when dealing with wide sense stationary (WSS) processes. This work introduces a novel class of positive definiteness ensuring likelihood-based estimators for Toeplitz structured covariance matrices (CMs) and their inverses. In order to accomplish this, we derive positive definiteness enforcing constraint sets for the Gohberg-Semencul (GS) parameterization of inverse symmetric Toeplitz matrices. Motivated by the relationship between the GS parameterization and autoregressive (AR) processes, we propose hyperparameter tuning techniques, which enable our estimators to combine advantages from state-of-the-art likelihood and non-parametric estimators. Moreover, we present a computationally cheap closed-form estimator, which is derived by maximizing an approximate likelihood. Due to the ensured positive definiteness, our estimators perform well for both the estimation of the CM and the inverse covariance matrix (ICM). Extensive simulation results validate the proposed estimators' efficacy for several standard Toeplitz structured CMs commonly employed in a wide range of applications.
摘要
当只有几个数据样本可用时，利用结构知识是必要的 для估计协方差矩阵和其 inverse。一个典型的例子是知道协方差矩阵是 toeplitz 结构的，这会发生在宽感Stationary (WSS) 过程中。这项工作介绍了一种新的正确性保证的可靠性基于 likelihood 的估计器，用于 toeplitz 结构协方差矩阵 (CM) 和其 inverse。为了完成这一点，我们 derivated positive definiteness 确保的约束集 для Gohberg-Semencul (GS) 参数化的 inverse symmetric toeplitz 矩阵。受到 GS 参数化和自动回归 (AR) 过程之间的关系的激励，我们提出了 hyperparameter 调整技术，这些技术使我们的估计器可以结合最好的 likelihood 和非 Parametric 估计器的优点。此外，我们提出了一种计算成本低的closed-form 估计器，它是通过最大化approximate likelihood 来 derivation。由于 Ensured positive definiteness，我们的估计器在估计 CM 和 inverse covariance matrix (ICM) 方面表现出色。我们的 simulate 结果表明，我们的估计器在一些标准的 toeplitz 结构 CM 上具有良好的性能。
Hybrid Precoding and Combining for mmWave Full-Duplex Joint Radar and Communication Systems under Self-Interference
paper_authors: Murat Bayraktar, Nuria González-Prelcic, Hao Chen
for: 这 paper 探讨了一种 joint radar and communication (JRC) 系统在 millimeter wave (mmWave) 频率上的设计，以实现同时感知和通信的功能。
methods: 该 paper 使用了 Generalized Eigenvalue-based 预编器，以满足下链用户速率、雷达增强和自遗浪谔抑制的多种需求。 hybrid 分析/_ digital 架构会削弱预编器中的自遗浪谔抑制能力，因此我们还提出了一种增强 SI 抑制的分析/_ digital 混合器。
results: 我们的数字实验表明，提posed 架构可以实现需要的雷达增强和自遗浪谔抑制，同时兼顾下链spectral efficiency的要求。 此外，我们的数字实验还表明，使用 OFDM 雷达处理技术可以实现高精度的距离和速度估计。Abstract
In the context of integrated sensing and communication (ISAC), a full-duplex (FD) transceiver can operate as a monostatic radar while maintaining communication capabilities. This paper investigates the design of precoders and combiners for a joint radar and communication (JRC) system at mmWave frequencies. The primary goals of the design are to minimize self-interference (SI) caused by FD operation, while guaranteeing certain performance in terms of some sensing and communication metrics, as well as taking into account the hardware limitations coming from a hybrid MIMO architecture. Specifically, we introduce a generalized eigenvalue-based precoder that takes into account downlink user rate, radar gain, and SI suppression. Since the hybrid analog/digital architecture degrades the SI suppression capability of the precoder, we further enhance SI suppression with the analog combiner. Our numerical results demonstrate that the proposed architecture achieves the required radar gain and SI mitigation while incurring a small loss in downlink spectral efficiency. Additionally, the numerical experiments also show that the use of orthogonal frequency division multiplexing (OFDM) for radar processing with the proposed beamforming architecture results in highly accurate range and velocity estimates for detected targets.
摘要
在 инте格рирован感知通信（ISAC）上，全双工（FD）扬送器可以作为单Statics radar 运行，保持通信能力。本文研究了 joint radar and communication（JRC）系统的 precoder 和 combiner 的设计，以实现 mmWave 频率下的自适应遮盲（SI）降低，保证感知和通信指标的一定性能，同时考虑 hybrid MIMO 架构的硬件限制。 Specifically, we introduce a generalized eigenvalue-based precoder that takes into account downlink user rate, radar gain, and SI suppression. Since the hybrid analog/digital architecture degrades the SI suppression capability of the precoder, we further enhance SI suppression with the analog combiner. Our numerical results demonstrate that the proposed architecture achieves the required radar gain and SI mitigation while incurring a small loss in downlink spectral efficiency. Additionally, the numerical experiments also show that the use of orthogonal frequency division multiplexing (OFDM) for radar processing with the proposed beamforming architecture results in highly accurate range and velocity estimates for detected targets.Note that Simplified Chinese is a more casual and informal version of Chinese, and it may not be appropriate for all situations or audiences. If you need a more formal version, you may want to consider using Traditional Chinese or Standard Chinese instead.