results: 论文实现了5.63dB的插入损耗（IL）和17.6%的三分之一带宽（FBW），表明薄膜 piezoelectric resonators可以在5G FR2 band上操作。Abstract
In this work, a 38.7 GHz acoustic wave ladder filter exhibiting insertion loss (IL) of 5.63 dB and 3-dB fractional bandwidth (FBW) of 17.6% is demonstrated, pushing the frequency limits of thin-film piezoelectric acoustic filter technology. The filter achieves operating frequency up to 5G millimeter wave (mmWave) frequency range 2 (FR2) bands, by thinning thin-film LiNbO3 resonators to sub-50 nm thickness. The high electromechanical coupling (k2) and quality factor (Q) of first-order antisymmetric (A1) mode resonators in 128 Y-cut lithium niobate (LiNbO3) collectively enable the first acoustic filters at mmWave. The key design consideration of electromagnetic (EM) resonances in interdigitated transducers (IDT) is addressed and mitigated. These results indicate that thin-film piezoelectric resonators could be pushed to 5G FR2 bands. Further performance enhancement and frequency scaling calls for better resonator technologies and EM-acoustic filter co-design.
摘要
在这项工作中，一种功率为38.7 GHz的声波级滤波器被实现，其插入损耗（IL）为5.63 dB，三分之一带宽（FBW）为17.6%。这种滤波器可以在2（FR2）频率段中操作，通过使用薄膜键石陶瓷（LiNbO3）共振器来减少膜厚至下50 nm。高电机电共振（k2）和质因子（Q）的首频模式共振器在128Y扁板键石陶瓷（LiNbO3）中共同实现了第一个声波滤波器在mmWave频率范围内。对声电共振器（IDT）中的电磁共振的设计考虑和控制也得到了解决。这些结果表明，薄膜键石陶瓷共振器可以在5G FR2频率段内操作。进一步提高性能和频率缩放需要更好的共振器技术和电磁-声波滤波器共设计。
Uncertainty-Aware Bayes’ Rule and Its Applications
for: This paper aims to address the issue of model misspecifications in prior distributions and/or data distributions, and to develop a generalized Bayes’ rule to combat these uncertainties.
methods: The paper proposes an uncertainty-aware Bayes’ rule, which upweights or downweights prior beliefs and data evidence based on the relative importance of prior and data distributions. The paper also derives three uncertainty-aware filtering algorithms: the uncertainty-aware Kalman filter, the uncertainty-aware particle filter, and the uncertainty-aware interactive multiple model filter.
results: The paper presents simulated and real-world experiments that demonstrate the superiority of the uncertainty-aware Bayes’ rule and the three uncertainty-aware filtering algorithms over the conventional Bayes’ rule and other state-of-the-art methods.Abstract
Bayes' rule has enabled innumerable powerful algorithms of statistical signal processing and statistical machine learning. However, when there exist model misspecifications in prior distributions and/or data distributions, the direct application of Bayes' rule is questionable. Philosophically, the key is to balance the relative importance of prior and data distributions when calculating posterior distributions: if prior (resp. data) distributions are overly conservative, we should upweight the prior belief (resp. data evidence); if prior (resp. data) distributions are overly opportunistic, we should downweight the prior belief (resp. data evidence). This paper derives a generalized Bayes' rule, called uncertainty-aware Bayes' rule, to technically realize the above philosophy, i.e., to combat the model uncertainties in prior distributions and/or data distributions. Simulated and real-world experiments showcase the superiority of the presented uncertainty-aware Bayes' rule over the conventional Bayes' rule: In particular, the uncertainty-aware Kalman filter, the uncertainty-aware particle filter, and the uncertainty-aware interactive multiple model filter are suggested and validated.
摘要
贝叶斯公式在统计信号处理和统计机器学习中实现了无数可能的强大算法。然而，当存在模型偏差在先后分布和/或数据分布中时，直接应用贝叶斯公式是有问题的。哲学上，关键是在计算后期分布时平衡先后分布和数据分布之间的相对重要性：如果先前分布（resp. 数据分布）太保守，我们应该增加先前信念（resp. 数据证据）的重要性；如果先前分布（resp. 数据分布）太机会主义，我们应该减少先前信念（resp. 数据证据）的重要性。这篇论文提出一种扩展的贝叶斯公式，called uncertainty-aware Bayes' rule，以技术实现上述哲学。通过实验和实际应用，论文显示了 uncertainty-aware Bayes' rule 的超越性，比如不确定性感知 kalman filter、不确定性感知 particle filter 和不确定性感知多模型过滤器。
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
results: 研究表明，EEG-DG比前一代方法更高效，具体来说是在一个模拟数据集和两个BCI竞赛数据集IV-2a和IV-2b上，EEG-DG的分类率分别为81.79%和87.12%，而且甚至超过了一些领域适应方法。Abstract
Motor imagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the classification is affected by the non-stationarity and individual variations of EEG signals. Simply pooling EEG data with different statistical distributions to train a classification model can severely degrade the generalization performance. To address this issue, the existing methods primarily focus on domain adaptation, which requires access to the target data during training. This is unrealistic in many EEG application scenarios. In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data. We optimize both the marginal and conditional distributions to ensure the stability of the joint distribution across source domains and extend it to a multi-source domain generalization framework to achieve domain-invariant feature representation, thereby alleviating calibration efforts. Systematic experiments on a simulative dataset and BCI competition datasets IV-2a and IV-2b demonstrate the superiority of our proposed EEG-DG over state-of-the-art methods. Specifically, EEG-DG achieves an average classification accuracy/kappa value of 81.79%/0.7572 and 87.12%/0.7424 on datasets IV-2a and IV-2b, respectively, which even outperforms some domain adaptation methods. Our code is available at https://github.com/XC-ZhongHIT/EEG-DG for free download and evaluation.
摘要
electromyography（EMG）幻象分类在无侵入式大脑-计算机交互（BCI）研究中扮演着关键性的角色。然而，分类受到EMG信号的非站点性和个体差异的影响。将EMG数据不同统计分布 pool 以train 分类模型可能导致极差的泛化性能。为解决这个问题，现有的方法主要集中在领域适应中，需要在训练过程中获取目标数据。这在许多EMG应用场景中是不现实的。在这篇论文中，我们提出了一种新的多源领域总结框架，称为EEG-DG，它利用不同统计分布的多个源领域来建立可靠的分类模型。我们同时优化了两个分布的独立和 Conditional distribution，以确保在源领域之间的联合分布的稳定性，并将其扩展为多源领域总结框架，以实现领域 invariant feature representation，从而减少准确化努力。系统性实验在一个模拟数据集和 BCIC 竞赛数据集 IV-2a 和 IV-2b 上表明，我们的提出的EEG-DG 超过了现有方法的性能。具体来说，EEG-DG 在 IV-2a 和 IV-2b 数据集上的平均分类精度/κ值为 81.79%/0.7572 和 87.12%/0.7424，甚至超过了一些领域适应方法。我们的代码可以免费下载和评估于 GitHub 上的 。
Joint Angle and Delay Cramér-Rao Bound Optimization for Integrated Sensing and Communications
results: numerical results 表明，优化的扩容设计可以实现出色的定位性能，并有效地减少了基站antenna的数量要求。Abstract
In this paper, we study a multi-input multi-output (MIMO) beamforming design in an integrated sensing and communication (ISAC) system, in which an ISAC base station (BS) is used to communicate with multiple downlink users and simultaneously the communication signals are reused for sensing multiple targets. Our interested sensing parameters are the angle and delay information of the targets, which can be used to locate these targets. Under this consideration, we first derive the Cram\'{e}r-Rao bound (CRB) for angle and delay estimation. Then, we optimize the transmit beamforming at the BS to minimize the CRB, subject to communication rate and power constraints. In particular, we obtain the optimal solution in closed-form in the case of single-target and single-user, and in the case of multi-target and multi-user scenario, the sparsity of the optimal solution is proven, leading to a reduction in computational complexity during optimization. The numerical results demonstrate that the optimized beamforming yields excellent positioning performance and effectively reduces the requirement for a large number of antennas at the BS.
摘要
在这篇论文中，我们研究了一种多输入多输出（MIMO）扩展探测通信（ISAC）系统中的扩展探测设计，其中一个ISAC基站（BS）用于通信多个下降用户，同时通信信号被重复用于探测多个目标。我们对于探测参数的 interessets是目标的角度和延迟信息，这些信息可以用来定位这些目标。在这种情况下，我们首先 derivethe Cramér-Rao bound（CRB） для角度和延迟估计。然后，我们在BS中优化发射扩展来最小化CRB，具体来说是subject to通信率和功率约束。在具体实现中，我们在单目标单用户情况下获得了closed-form的优化解，而在多目标多用户情况下，我们证明了优化解的稀疏性，从而降低了计算复杂性。numerical results表明，优化的扩展探测可以很好地定位性能和减少了BS需要的antenna数量。
Energy-Efficient Analog Beamforming for RF-WET with Charging Time Constraint
results: 研究结果表明，相比往常的参考方案，我们的RF-WET策略可以更好地为IoT设备提供能量，而且与antenna数量增加时，性能越来越好。Abstract
Internet of Things (IoT) sustainability may hinge on radio frequency wireless energy transfer (RF-WET). However, energy-efficient charging strategies are still needed, motivating our work. Specifically, this letter proposes a time division scheme to efficiently charge low-power devices in an IoT network. For this, a multi-antenna power beacon (PB) drives the devices' energy harvesting circuit to the highest power conversion efficiency point via energy beamforming, thus achieving minimum energy consumption. Herein, we adopt the analog multi-antenna architecture due to its low complexity, cost, and energy consumption. The proposal includes a simple yet accurate model for the transfer characteristic of the energy harvesting circuit, enabling the optimization framework. The results evince the effectiveness of our RF-WET strategy over a benchmark scheme where the PB charges all the IoT devices simultaneously. Furthermore, the performance increases with the number of PB antennas.
摘要
互联网智能物件（IoT）可持续性可能取决于无线频率无线能量传输（RF-WET）。然而，仍需要能效的充电策略，这使我们的工作感到推动。特别是，这封信函描述了一种时分多址方案，以高效地充电低功率设备在IoT网络中。在这个方案中，一个多antenna能量扩散器（PB）驱动设备的能量从抽取到最高熵转换效率点，以获得最小的能量消耗。我们采用了分析多antenna架构，因为它具有低的复杂度、成本和能量消耗。我们的提案包括一个简单又准确的转换特性模型，实现优化框架。结果显示了我们的RF-WET策略比对benchmark方案，在充电所有IoT设备的情况下更有效。此外，性能随着PB天线的数量增加。
Empowering high-dimensional optical fiber communications with integrated photonic processors
results: 实验表明，该系统可以高效地处理六个空间和波分谱模式，并且可以高质量地生成电子信号。Abstract
Mode division multiplexing (MDM) in optical fibers enables multichannel capabilities for various applications, including data transmission, quantum networks, imaging, and sensing. However, MDM optical fiber systems, usually necessities bulk-optics approaches for launching different orthogonal fiber modes into the multimode optical fiber, and multiple-input multiple-output digital electronic signal processing at the receiver side to undo the arbitrary mode scrambling in a circular-core optical fiber. Here we show that a high-dimensional optical fiber communication system can be entirely implemented by a reconfigurable integrated photonic processor, featuring kernels of multichannel mode multiplexing transmitter and all-optical descrambling receiver. High-speed and inter-chip communications involving six spatial- and polarization modes have been experimentally demonstrated with high efficiency and high-quality eye diagrams, despite the presence of random mode scrambling and polarization rotation in a circular-core few-mode fiber. The proposed photonic integration approach holds promising prospects for future space-division multiplexing applications.
摘要
Here, we demonstrate that a high-dimensional optical fiber communication system can be entirely implemented by a reconfigurable integrated photonic processor, featuring kernels of multichannel mode multiplexing transmitter and all-optical descrambling receiver. High-speed and inter-chip communications involving six spatial- and polarization modes have been experimentally demonstrated with high efficiency and high-quality eye diagrams, despite the presence of random mode scrambling and polarization rotation in a circular-core few-mode fiber.The proposed photonic integration approach holds promising prospects for future space-division multiplexing applications.中文翻译：Mode division multiplexing（MDM）在光纤中实现多个通道，用于数据传输、量子网络、成像和探测等应用。然而，现有的MDM光纤系统通常需要使用填充光学方法将不同的平行光纤模式入库多模光纤，以及接收端多输入多输出的数字电子处理器来解除圆柱形光纤中的随机模式混乱。在这里，我们展示了一种完全由可重新配置的光子处理器实现的高维度光纤通信系统，包括多模式多plexing发射器和全光学减少接收器。我们在实验中成功地实现了六个空间和极化模式之间的高速交换和 между板通信，并且具有高效率和高质量眼agram。尽管存在圆柱形少模光纤中的随机模式混乱和极化转换，但是我们的光子集成方法仍然保持了未来空间分多plexing应用的良好前景。
Few-Shot Recognition and Classification of Jamming Signal via CGAN-Based Fusion CNN Algorithm
results: 比前一代方法提高8%的准确率，并在有限的数据集上进行了验证。通过使用实际的卫星通信场景的硬件平台进行模拟，并对时域信号数据进行验证，实验结果表明我们的算法在实际通信场景中仍然表现出色。Abstract
The precise classification of jamming signals holds paramount significance in the effective implementation of anti-jamming strategies within communication systems subject to intricate environmental variables. In light of this imperative, we propose an innovative fusion algorithm based on conditional generative adversarial network (CGAN) and convolutional neural network (CNN) to solve the problem of difficulty in applying deep learning (DL) algorithms due to the instantaneous nature of jamming signals in practical communication systems. Compared with previous methods, our algorithm achieved an 8% improvement in accuracy even when working with a limited dataset. Unlike previous research, we have simulated real-world satellite communication scenarios using a hardware platform and validated our algorithm using the resulting time-domain waveform data. The experimental results indicate that our algorithm still performs extremely well, which demonstrates significant potential for practical application in real-world communication scenarios.
摘要
“ jamming 信号的精确分类对于实现有效的反干扰策略在受到复杂环境变量的通信系统中具有极高的重要性。在这一点上，我们提出了一种基于条件生成 adversarial network (CGAN) 和卷积神经网络 (CNN) 的创新融合算法，以解决深度学习 (DL) 算法在实际通信系统中应用时的困难。与前一代方法相比，我们的算法在有限数据集上实现了8%的提升精度。不同于前一些研究，我们在硬件 пла台上模拟了真实的卫星通信场景，并使用时域波形数据验证了我们的算法。实验结果表明，我们的算法在实际通信场景中仍然表现出色，这表明它在实际应用中具有极高的潜力。”Note: The translation is in Simplified Chinese, which is the standard form of Chinese used in mainland China and Singapore. If you need Traditional Chinese, please let me know.
for: This paper is to study the application of delay Doppler transform (DDT) in time domain signals.
methods: The study uses delay Doppler transform (DDT) to describe the delay and Doppler issues in time domain signals.
results: The research finds that DDT can help us better understand the characteristics of delay Doppler channels and provide practical performance evaluation methods.Abstract
This letter is to introduce delay Doppler transform (DDT) for a time domain signal. It is motivated by the recent studies in wireless communications over delay Doppler channels that have both time and Doppler spreads, such as, satellite communication channels. We present some simple properties of DDT as well. The DDT study may provide insights of delay Doppler channels.
摘要
这封信是为引入延迟Doppler变换（DDT），用于处理时域信号。这是由于最近关于无线通信频率上的延迟Doppler通道的研究而出发的，这些通道具有时间和Doppler扩散。我们将介绍一些简单的DDT性质，以及它们在延迟Doppler通道上的应用。这些研究可能会为延迟Doppler通道提供新的思路。
Coverage and Rate Analysis for Cell-Free LEO Satellite Networks
paper_authors: Xiangyu Li, Bodong Shang, Na Deng, Shanzhi Chen
for: investigate an architecture of cell-free (CF) LEO satellite (CFLS) networks from a system-level perspective to improve quality-of-service (QoS)
methods: use multiple satellites to serve a user, and analyze the coverage and rate of a typical user in the CFLS network
results: the CFLS network achieves a higher coverage probability than the traditional single satellite-supported network, and user’s ergodic rate is maximized by selecting an appropriate number of serving satellites.Here’s the full text in Simplified Chinese:
for: 这篇论文是 investigate cell-free (CF) LEO satellite (CFLS) 网络的系统层次设计，以提高质量服务 (QoS)
methods: 使用多颗卫星服务用户，并分析CFLS 网络中典型用户的覆盖率和速率
results: CFLS 网络的覆盖率高于传统单颗卫星支持的网络，用户的平均速率可以通过选择合适的服务卫星来最大化。Abstract
Low-earth orbit (LEO) satellite communication is one of the enabling key technologies in next-generation (6G) networks. However, single satellite-supported downlink communication may not meet user's needs due to limited signal strength, especially in emergent scenarios. In this letter, we investigate an architecture of cell-free (CF) LEO satellite (CFLS) networks from a system-level perspective, where a user can be served by multiple satellites to improve its quality-of-service (QoS). Furthermore, we analyze the coverage and rate of a typical user in the CFLS network. Simulation and numerical results show that the CFLS network achieves a higher coverage probability than the traditional single satellite-supported network. Moreover, user's ergodic rate is maximized by selecting an appropriate number of serving satellites.
摘要
Integrated Sensing and Communication for Network-Assisted Full-Duplex Cell-Free Distributed Massive MIMO Systems
paper_authors: Fan Zeng, Jingxuan Yu, Jiamin Li, Feiyang Liu, Dongming Wang, Xiaohu You
for: 本研究旨在实现Integrated Sensing and Communication（ISAC）系统， combining network-assisted full-duplex（NAFD）技术和分布式雷达探测。
methods: 该系统采用了具有通信和探测能力的下行和上行远程广播单元（RRU）。
results: 对比其他ISAC方案，提出的方案可提供更稳定的探测和更好的通信性能。此外，提出了两种功率分配算法，可以同时优化通信和探测性能。Abstract
In this paper, we combine the network-assisted full-duplex (NAFD) technology and distributed radar sensing to implement integrated sensing and communication (ISAC). The ISAC system features both uplink and downlink remote radio units (RRUs) equipped with communication and sensing capabilities. We evaluate the communication and sensing performance of the system using the sum communication rates and the Cramer-Rao lower bound (CRLB), respectively. We compare the performance of the proposed scheme with other ISAC schemes, the result shows that the proposed scheme can provide more stable sensing and better communication performance. Furthermore, we propose two power allocation algorithms to optimize the communication and sensing performance jointly. One algorithm is based on the deep Q-network (DQN) and the other one is based on the non-dominated sorting genetic algorithm II (NSGA-II). The proposed algorithms provide more feasible solutions and achieve better system performance than the equal power allocation algorithm.
摘要
在这篇论文中，我们将网络协助全双工（NAFD）技术和分布式雷达探测结合，实现集成探测通信（ISAC）系统。ISAC系统包括上行和下行远程广播单元（RRU），各自携带通信和探测能力。我们使用总通信速率和克拉默-拉奥lower bound（CRLB）评估系统的通信和探测性能。与其他ISAC方案相比，我们的方案可以提供更稳定的探测和更好的通信性能。此外，我们提出了两种功率分配算法来优化通信和探测性能：一种是基于深度Q网络（DQN），另一种是基于非通过遗传算法II（NSGA-II）。这两种算法可以提供更实际的解决方案，并且可以在系统性能上做出更好的优化。