results: 我们的数值结果表明，提案的方案在 both uplink和downlink传输中可以获得显著的性能改进（相比于其他方案）。Abstract
Channel state information (CSI) estimation is a critical issue in the design of modern massive multiple-input multiple-output (mMIMO) networks. With the increasing number of users, assigning orthogonal pilots to everyone incurs a large overhead that strongly penalizes the system's spectral efficiency (SE). It becomes thus necessary to reuse pilots, giving rise to pilot contamination, a vital performance bottleneck of mMIMO networks. Reusing pilots among the users of the same cell is a desirable operation condition from the perspective of reducing training overheads; however, the intra-cell pilot contamination might worsen due to the users' proximity. Reconfigurable intelligent surfaces (RISs), capable of smartly controlling the wireless channel, can be leveraged for intra-cell pilot reuse. In this paper, our main contribution is a RIS-aided approach for intra-cell pilot reuse and the corresponding channel estimation method. Relying upon the knowledge of only statistical CSI, we optimize the RIS phase shifts based on a manifold optimization framework and the RIS positioning based on a deterministic approach. The extensive numerical results highlight the remarkable performance improvements the proposed scheme achieves (for both uplink and downlink transmissions) compared to other alternatives.
摘要
To mitigate the impact of pilot contamination, this paper proposes a RIS-aided approach for intra-cell pilot reuse and a corresponding channel estimation method. The approach relies on statistical CSI and optimizes the RIS phase shifts using a manifold optimization framework. Additionally, the RIS positioning is determined using a deterministic approach.The proposed scheme achieves significant performance improvements compared to other alternatives, as demonstrated through extensive numerical results. The improvements are evident in both uplink and downlink transmissions. The use of RISs enables the efficient reuse of pilots, reducing the training overhead and improving the system's overall performance.
Longitudinal gOSNR Monitoring by Receiver-side Digital Signal Processing in Multi-Span Optical Transmission System
results: 实验表明，该方法可以准确地估计链路上的gOSNR，并且可以在12个Span链路中进行实验验证。Abstract
We propose the world first longitudinal gOSNR estimation by using correlation template method at Rx, without any monitoring devices located in the middle of the link. The proposed method is experimentally demonstrated in a 12-span link with commercial transceiver.
摘要
我们提出了全球首个长itudinal gOSNR估计方法，使用相关模板方法在Rx中进行估计，无需在链接中间设置监测设备。我们的方法在12 span链接中进行实验，并使用商业转发器。Here's a breakdown of the translation:* 全球首个 (gOSNR) - 全球首个 refers to the fact that this is the first method in the world to estimate gOSNR.* 长itudinal (长itudinal) - 长itudinal refers to the fact that the method is used to estimate the gOSNR in the longitudinal direction.* 估计方法 (估计方法) - 估计方法 refers to the method used to estimate the gOSNR.* 使用相关模板方法 (使用相关模板方法) - 使用相关模板方法 refers to the fact that the method uses a correlation template to estimate the gOSNR.* 在Rx中进行估计 (在Rx中进行估计) - 在Rx中进行估计 refers to the fact that the method estimates the gOSNR at the receiver (Rx) side.* 无需在链接中间设置监测设备 (无需在链接中间设置监测设备) - 无需在链接中间设置监测设备 refers to the fact that the method does not require any monitoring devices to be installed in the middle of the link.* 我们的方法 (我们的方法) - 我们的方法 refers to the fact that this is a method proposed by the speaker.* 在12 span链接中进行实验 (在12 span链接中进行实验) - 在12 span链接中进行实验 refers to the fact that the method was experimentally demonstrated in a 12-span link.* 并使用商业转发器 (并使用商业转发器) - 并使用商业转发器 refers to the fact that the method used commercial transceivers in the experiment.
Joint Coding-Modulation for Digital Semantic Communications via Variational Autoencoder
results: 实验结果表明，该提议的联合编码-模ulation框架在不同的通道条件、传输率和模ulation顺序下，比 sepate设计Semantic编码和模ulation的方法表现更好，并且与analog Semantic通信的性能差距随模ulation顺序的增加而减少。Abstract
Semantic communications have emerged as a new paradigm for improving communication efficiency by transmitting the semantic information of a source message that is most relevant to a desired task at the receiver. Most existing approaches typically utilize neural networks (NNs) to design end-to-end semantic communication systems, where NN-based semantic encoders output continuously distributed signals to be sent directly to the channel in an analog communication fashion. In this work, we propose a joint coding-modulation framework for digital semantic communications by using variational autoencoder (VAE). Our approach learns the transition probability from source data to discrete constellation symbols, thereby avoiding the non-differentiability problem of digital modulation. Meanwhile, by jointly designing the coding and modulation process together, we can match the obtained modulation strategy with the operating channel condition. We also derive a matching loss function with information-theoretic meaning for end-to-end training. Experiments conducted on image semantic communication validate that our proposed joint coding-modulation framework outperforms separate design of semantic coding and modulation under various channel conditions, transmission rates, and modulation orders. Furthermore, its performance gap to analog semantic communication reduces as the modulation order increases while enjoying the hardware implementation convenience.
摘要
semantic communication 已经emerged as a new paradigm to improve communication efficiency by transmitting the most relevant semantic information of a source message to a desired task at the receiver. Most existing approaches typically use neural networks (NNs) to design end-to-end semantic communication systems, where NN-based semantic encoders output continuously distributed signals to be sent directly to the channel in an analog communication fashion.在这项工作中，我们提出了一个joint coding-modulation框架 для数字semantic communication，使用variational autoencoder (VAE)。我们的方法学习源数据到离散符号的过渡概率，从而避免了数字模调不 diferenciability问题。同时，我们通过结合编码和模调过程的共同设计，可以匹配获得的模调策略与运行的通道条件。我们还 derivate一个匹配损失函数with information-theoretic meaning for end-to-end培训。实验在图像semantic communication中 validate that our proposed joint coding-modulation framework outperforms separate design of semantic coding and modulation under various channel conditions, transmission rates, and modulation orders. 此外，我们发现，当modulation order增加时，我们的性能与analog semantic communication的差距逐渐减小，而且享有硬件实现的便利。
Near and Far Field Model Mismatch: Implications on 6G Communications, Localization, and Sensing
results: 研究结果表明，NF模型与FF模型之间的差异可能导致系统性能指标如地位精度、感知可靠性和通信效率的下降。Abstract
The upcoming 6G technology is expected to operate in near-field (NF) radiating conditions thanks to high-frequency and electrically large antenna arrays. While several studies have already addressed this possibility, it is worth noting that NF models introduce heightened complexity, the justification for which is not always evident in terms of performance improvements. Therefore, this paper delves into the implications of the disparity between NF and far-field (FF) models concerning communication, localization, and sensing systems. Such disparity might lead to a degradation of performance metrics like localization accuracy, sensing reliability, and communication efficiency. Through an exploration of the effects arising from the mismatches between NF and FF models, this study seeks to illuminate the challenges confronting system designers and offer valuable insights into the balance between model accuracy, which typically requires a high complexity and achievable performance. To substantiate our perspective, we also incorporate a numerical performance assessment confirming the repercussions of the mismatch between NF and FF models.
摘要
预计6G技术即将在近场（NF）发射条件下运行，由于高频和电动巨大的天线阵列。虽然已有一些研究对这一可能性进行了评估，但值得注意的是，NF模型会带来更高的复杂性，其性能改善的依据并不总是明显。因此，本文探讨NF和FF模型之间的差异对通信、地位确定和探测系统的影响。这种差异可能会导致本地化精度、探测可靠性和通信效率的下降。通过研究NF和FF模型之间的差异的影响，本文旨在披露系统设计师面临的挑战并提供有价值的思路，以寻求在精度和实现性之间寻找平衡。为证明我们的观点，我们还包括一个数字性能评估，证明NF和FF模型之间的差异带来的后果。
3D Non-Stationary Channel Measurement and Analysis for MaMIMO-UAV Communications
results: 文章通过测量和分析了频率域、时间域和空间域的渠道统计特性，包括能量延迟观测图（PDP）、常规干扰和时域频率域的干扰率。此外，文章还提出了定向角（SA）作为时域站点距离的补充指标，并分析了频率域的干扰宽渠和RMS延迟扩散。最后，文章分析了MaMIMO数组元素之间的空间相关性，以证明MaMIMO-UAV通信系统的空间站点准确性。Abstract
Unmanned aerial vehicles (UAVs) have gained popularity in the communications research community because of their versatility in placement and potential to extend the functions of communication networks. However, there remains still a gap in existing works regarding detailed and measurement-verified air-to-ground (A2G) Massive Multi-Input Multi-Output (MaMIMO) channel characteristics which play an important role in realistic deployment. In this paper, we first design a UAV MaMIMO communication platform for channel acquisition. We then use the testbed to measure uplink Channel State Information (CSI) between a rotary-wing drone and a 64-element MaMIMO base station (BS). For characterization, we focus on multidimensional channel stationarity which is a fundamental metric in communication systems. Afterward, we present measurement results and analyze the channel statistics based on power delay profiles (PDPs) considering space, time, and frequency domains. We propose the stationary angle (SA) as a supplementary metric of stationary distance (SD) in the time domain. We analyze the coherence bandwidth and RMS delay spread for frequency stationarity. Finally, spatial correlations between elements are analyzed to indicate the spatial stationarity of the array. The space-time-frequency channel stationary characterization will benefit the physical layer design of MaMIMO-UAV communications.
摘要
无人飞行器（UAV）在通信研究领域得到了广泛的应用，主要是因为它们的位置灵活性和通信网络功能扩展的潜力。然而，现有的研究还存在一个空白，即详细和测量确认的空中到地面（A2G）大量多输入多输出（MaMIMO）通道特性的研究。在这篇论文中，我们首先设计了一个无人飞行器MaMIMO通信平台，然后使用测试床测量了旋翼飞机和64个MaMIMO基站（BS）之间的上行频率响应（CSI）。对于Characterization，我们将注重多维度通道Stationarity，这是通信系统中的基本指标之一。接着，我们提供了测量结果和分析频率响应的Channel Statistics，考虑了空间、时间和频率频率域。此外，我们还分析了天线元素之间的空间相关性，以评估MaMIMO通信系统的空间站点性。最后，我们提出了“站点角度”（SA）作为时域站点距离（SD）的补充指标，并分析了宽bandwidth和RMS延迟扩散。空间时间频率通道Stationarity的Characterization将有助于MaMIMO-UAV通信physical层设计。
ChannelComp: A General Method for Computation by Communications
paper_authors: Saeed Razavikia, José Mairton Barros Da Silva Júnior, Carlo Fischione
For: The paper proposes a new digital channel computing method named ChannelComp, which can use digital as well as analog modulations, and can achieve arbitrary function computation over-the-air.* Methods: The proposed method uses a feasibility optimization problem to ascertain the optimal modulation for computing arbitrary functions over-the-air, and proposes pre-coders to adapt existing digital modulation schemes for computing the function over the multiple access channel.* Results: The simulation results show that ChannelComp outperforms AirComp, particularly for product functions, with more than 10 dB improvement of the computation error.Here is the text in Simplified Chinese:* For: 本文提出了一种新的数字通道计算方法 named ChannelComp，可以使用数字和分布式模ulation，实现空中进行任意函数计算。* Methods: 提议的方法使用一个可行优化问题确定空中计算任意函数的最佳模ulation，并提出适应现有数字模ulation schemes来计算函数 sobre 多重存取通道。* Results: 实验结果表明，ChannelComp 比 AirComp 更高效，特别是对乘积函数，计算错误下降了More than 10 dB。Abstract
Over-the-air computation (AirComp) is a well-known technique by which several wireless devices transmit by analog amplitude modulation to achieve a sum of their transmit signals at a common receiver. The underlying physical principle is the superposition property of the radio waves. Since such superposition is analog and in amplitude, it is natural that AirComp uses analog amplitude modulations. Unfortunately, this is impractical because most wireless devices today use digital modulations. It would be highly desirable to use digital communications because of their numerous benefits, such as error correction, synchronization, acquisition of channel state information, and widespread use. However, when we use digital modulations for AirComp, a general belief is that the superposition property of the radio waves returns a meaningless overlapping of the digital signals. In this paper, we break through such beliefs and propose an entirely new digital channel computing method named ChannelComp, which can use digital as well as analog modulations. We propose a feasibility optimization problem that ascertains the optimal modulation for computing arbitrary functions over-the-air. Additionally, we propose pre-coders to adapt existing digital modulation schemes for computing the function over the multiple access channel. The simulation results verify the superior performance of ChannelComp compared to AirComp, particularly for the product functions, with more than 10 dB improvement of the computation error.
摘要
频段计算（AirComp）是一种已知的技术，多个无线设备通过模拟幅度模ulation来实现一个共同接收器上的 signals的总和。物理原理是无线波的积加性质。由于这种积加是Analog的，因此AirComp通常使用Analog幅度模ulation。然而，这是不实用的，因为今天大多数无线设备使用数字模ulation。使用数字模ulation可以获得许多优点，如错误恢复、同步、通道状态信息获取和广泛使用。但是，当用数字模ulation进行频段计算时，通常认为无线波的积加性质返回无意义的数字信号的重叠。在这篇论文中，我们突破这些信念，并提出一种全新的数字通道计算方法，名为ChannelComp，可以使用数字和Analog模ulation。我们提出一个可行优化问题，以确定在空中计算任意函数的最佳模ulation。此外，我们还提出适应器，以适应现有的数字模ulation方案来计算函数在多接收器通道上。实验结果表明ChannelComp比AirComp表现更优，特别是对于乘法函数，错误率下降了10dB以上。
Plane Constraints Aided Multi-Vehicle Cooperative Positioning Using Factor Graph Optimization
results: 提高了定位性能，特别是在车辆间距测量受到中断时表现出色。Abstract
The development of vehicle-to-vehicle (V2V) communication facil-itates the study of cooperative positioning (CP) techniques for vehicular applications. The CP methods can improve the posi-tioning availability and accuracy by inter-vehicle ranging and data exchange between vehicles. However, the inter-vehicle rang-ing can be easily interrupted due to many factors such as obsta-cles in-between two cars. Without inter-vehicle ranging, the other cooperative data such as vehicle positions will be wasted, leading to performance degradation of range-based CP methods. To fully utilize the cooperative data and mitigate the impact of inter-vehicle ranging loss, a novel cooperative positioning method aided by plane constraints is proposed in this paper. The positioning results received from cooperative vehicles are used to construct the road plane for each vehicle. The plane parameters are then introduced into CP scheme to impose constraints on positioning solutions. The state-of-art factor graph optimization (FGO) algo-rithm is employed to integrate the plane constraints with raw data of Global Navigation Satellite Systems (GNSS) as well as inter-vehicle ranging measurements. The proposed CP method has the ability to resist the interruptions of inter-vehicle ranging since the plane constraints are computed by just using position-related data. A vehicle can still benefit from the position data of cooperative vehicles even if the inter-vehicle ranging is unavaila-ble. The experimental results indicate the superiority of the pro-posed CP method in positioning performance over the existing methods, especially when the inter-ranging interruptions occur.
摘要
发展交通自动化技术的车辆到车辆通信（V2V）技术为汽车应用增加了可靠性和精度。但是，在两辆车之间的距离测量中，可能会有各种障碍物，导致距离测量中断。在没有距离测量的情况下，其他合作数据，如车辆位置，将被浪费，从而导致距离基于CP方法的性能下降。为了充分利用合作数据和减少距离测量中断的影响，本文提出了一种基于平面约束的新型合作定位方法。received from cooperative vehicles are used to construct the road plane for each vehicle. The plane parameters are then introduced into CP scheme to impose constraints on positioning solutions. The state-of-art factor graph optimization (FGO) algorithm is employed to integrate the plane constraints with raw data of Global Navigation Satellite Systems (GNSS) as well as inter-vehicle ranging measurements. The proposed CP method has the ability to resist the interruptions of inter-vehicle ranging since the plane constraints are computed by just using position-related data. A vehicle can still benefit from the position data of cooperative vehicles even if the inter-vehicle ranging is unavailable. The experimental results indicate the superiority of the proposed CP method in positioning performance over the existing methods, especially when the inter-ranging interruptions occur.Here's the translation in Traditional Chinese:随着交通自动化技术的发展，车辆通信（V2V）技术对汽车应用增加了可靠性和精度。但是，在两辆车之间的距离测量中，可能会有各种障碍物，导致距离测量中断。在没有距离测量的情况下，其他合作数据，如车辆位置，将被浪费，从而导致距离基于CP方法的性能下降。为了充分利用合作数据和减少距离测量中断的影响，本文提出了一种基于平面约束的新型合作定位方法。received from cooperative vehicles are used to construct the road plane for each vehicle. The plane parameters are then introduced into CP scheme to impose constraints on positioning solutions. The state-of-art factor graph optimization (FGO) algorithm is employed to integrate the plane constraints with raw data of Global Navigation Satellite Systems (GNSS) as well as inter-vehicle ranging measurements. The proposed CP method has the ability to resist the interruptions of inter-vehicle ranging since the plane constraints are computed by just using position-related data. A vehicle can still benefit from the position data of cooperative vehicles even if the inter-vehicle ranging is unavailable. The experimental results indicate the superiority of the proposed CP method in positioning performance over the existing methods, especially when the inter-ranging interruptions occur.
ISAC 4D Imaging System Based on 5G Downlink Millimeter Wave Signal
results: 我们的 simulations 表明，我们的提议的方法可以提供更好的成像结果。代码可以在https://github.com/MrHaobolu/ISAC_4D_Imaging.git中获取。Abstract
Integrated Sensing and Communication(ISAC) has become a key technology for the 5th generation (5G) and 6th generation (6G) wireless communications due to its high spectrum utilization efficiency. Utilizing infrastructure such as 5G Base Stations (BS) to realize environmental imaging and reconstruction is important for promoting the construction of smart cities. Current 4D imaging methods utilizing Frequency Modulated Continuous Wave (FMCW) based Fast Fourier Transform (FFT) are not suitable for ISAC scenarios due to the higher bandwidth occupation and lower resolution. We propose a 4D (3D-Coordinates, Velocity) imaging method with higher sensing accuracy based on 2D-FFT with 2D-MUSIC utilizing standard 5G Downlink (DL) millimeter wave (mmWave) signals. To improve the sensing precision we also design a transceiver antenna array element arrangement scheme based on MIMO virtual aperture technique. We further propose a target detection algorithm based on multi-dimensional Constant False Alarm (CFAR) detection, which optimizes the ISAC imaging signal processing flow and reduces the computational pressure of signal processing. Simulation results show that our proposed method has better imaging results. The code is publicly available at https://github.com/MrHaobolu/ISAC\_4D\_IMaging.git.
摘要
Integrated Sensing and Communication(ISAC) 已成为 fifth generation (5G) 和 sixth generation (6G) 无线通信技术的关键因素，具有高频率使用效率。通过使用 5G 基站 (BS) 实现环境成像和重建是推动智能城市建设的关键。现有的 4D 成像方法使用 Frequency Modulated Continuous Wave (FMCW) 基于 Fast Fourier Transform (FFT) 不适合 ISAC 场景，因为它们占用更高频率和分辨率较低。我们提议一种基于 2D-FFT 和 2D-MUSIC 的 4D (3D-坐标、速度) 成像方法，可以提高成像精度。我们还设计了一种基于 MIMO 虚拟天线技术的天线元件顺序安排方案，以提高探测精度。此外，我们还提出了一种基于多维度 Constant False Alarm (CFAR) 检测算法的目标检测算法，可以优化 ISAC 成像信号处理流程，并减少信号处理的计算压力。实验结果表明，我们的提议方法可以获得更好的成像效果。代码可以在 上获取。
Mutual Information Metrics for Uplink MIMO-OFDM Integrated Sensing and Communication System
results: Monte Carlo 仿真结果显示，与其他波形优化方案相比，提议的ISAC方案在总性性能方面表现最佳。Abstract
As the uplink sensing has the advantage of easy implementation, it attracts great attention in integrated sensing and communication (ISAC) system. This paper presents an uplink ISAC system based on multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) technology. The mutual information (MI) is introduced as a unified metric to evaluate the performance of communication and sensing. In this paper, firstly, the upper and lower bounds of communication and sensing MI are derived in details based on the interaction between communication and sensing. And the ISAC waveform is optimized by maximizing the weighted sum of sensing and communication MI. The Monte Carlo simulation results show that, compared with other waveform optimization schemes, the proposed ISAC scheme has the best overall performance.
摘要
《这个发送感知系统（ISAC）基于多入多出多频分配（MIMO-OFDM）技术，具有易于实现的优点。这篇论文首先 derive了通信和感知之间的互动的上下限，然后将ISAC波形优化为最大化混合通信和感知的干扰情况。实验结果显示，与其他波形优化方案相比，提案的ISAC方案在总性能方面表现最佳。》Here's the translation breakdown:* 这个 (tī gò) - this* 发送感知系统 (fā sòng gǎn jí xìng zhì) - uplink sensing and communication system* 基于 (jī yú) - based on* 多入多出多频分配 (duō rù duō chū duō fēn fāng) - multi-input multi-output orthogonal frequency division multiplexing* 技术 (jì shù) - technology* 具有 (gù yǒu) - has* 易于 (qǐ yú) - easy* 实现 (shí jì) - implementation* 这篇 (zhè běn) - this paper* 论文 (lùn wén) - paper* 首先 (chū xiān) - firstly* derive (dì zhì) - derive* 通信 (tōng xìn) - communication* 感知 (gǎn jí) - sensing* 之间 (zhī jiān) - between* 上下限 (shàng xià jiàn) - upper and lower bounds* 然后 (rán hái) - then* 将ISAC波形 (jiāng ISAC bā xíng) - optimize the ISAC waveform* 优化 (yōu zuò) - optimize* 为 (wèi) - for* 最大化 (zuò dà jiā) - maximize* 混合 (hù hé) - mixed* 通信 (tōng xìn) - communication* 感知 (gǎn jí) - sensing* 干扰 (gān jí) - interference* 情况 (qíng jì) - situation* 实验 (shí yan) - experiment* 结果 (jié guō) - results* 显示 (xiǎn shi) - show* 与 (yǔ) - with* 其他 (qí tè) - other* 波形 (bā xíng) - waveform* 优化方案 (yōu zuò fāng àn) - optimization schemes* 相比 (xiāng bǐ) - compared with* 表现 (biǎo xiǎng) - performance* 最佳 (zuò jiā) - best* 总性能 (zǒng xìng néng) - overall performance
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning
results: 57% 低于基准值的位置误差变量（57% lower positioning error variance compared to a beam-scanning baseline）Abstract
Positioning is an essential service for various applications and is expected to be integrated with existing communication infrastructures in 5G and 6G. Though current Wi-Fi and cellular base stations (BSs) can be used to support this integration, the resulting precision is unsatisfactory due to the lack of precise control of the wireless signals. Recently, BSs adopting reconfigurable holographic surfaces (RHSs) have been advocated for positioning as RHSs' large number of antenna elements enable generation of arbitrary and highly-focused signal beam patterns. However, existing designs face two major challenges: i) RHSs only have limited operating bandwidth, and ii) the positioning methods cannot adapt to the diverse environments encountered in practice. To overcome these challenges, we present HoloFed, a system providing high-precision environment-adaptive user positioning services by exploiting multi-band(MB)-RHS and federated learning (FL). For improving the positioning performance, a lower bound on the error variance is obtained and utilized for guiding MB-RHS's digital and analog beamforming design. For better adaptability while preserving privacy, an FL framework is proposed for users to collaboratively train a position estimator, where we exploit the transfer learning technique to handle the lack of position labels of the users. Moreover, a scheduling algorithm for the BS to select which users train the position estimator is designed, jointly considering the convergence and efficiency of FL. Our simulation results confirm that HoloFed achieves a 57% lower positioning error variance compared to a beam-scanning baseline and can effectively adapt to diverse environments.
摘要
positioning是一种重要的服务，用于各种应用程序，预计在5G和6G中与现有的通信基础设施集成。尽管当前的Wi-Fi和mobile基站可以用来支持这种集成，但由于无线信号的精度控制的缺失，所得到的精度不够高。随后，使用可重新配置的干扰表面(RHS)的基站被提议用于位置服务，因为RHS的大量天线元素可以生成自由的信号扫描方式。然而，现有的设计遇到了两个主要挑战：一是RHS只有有限的运作频率，二是位置方法无法适应实际中遇到的多样化环境。为了解决这些挑战，我们提出了HoloFed系统，该系统通过多频（MB）-RHS和联合学习（FL）技术提供高精度环境适应用户位置服务。为了提高位置性能，我们 obtener un lower bound on the error variance and utilizarlo para guiar el diseño de la formación digital y analógica de MB-RHS。另外，我们提出了一个用户协作 trains a position estimator的FL框架，其中我们利用了传输学习技术来处理用户没有位置标签的问题。此外，我们设计了一种BS选择用户训练位置估计器的分配算法，同时考虑了FL的 converges和效率。our simulation results confirm that HoloFed achieves a 57% lower positioning error variance compared to a beam-scanning baseline and can effectively adapt to diverse environments.
Streaming Probabilistic PCA for Missing Data with Heteroscedastic Noise
paper_authors: Kyle Gilman, David Hong, Jeffrey A. Fessler, Laura Balzano
for: This paper aims to develop a novel algorithm for principal component analysis (PCA) in streaming data with missing entries and heteroscedastic noise.
methods: The proposed algorithm, called Streaming Heteroscedastic Algorithm for PCA (SHASTA-PCA), uses a stochastic alternating expectation maximization approach to jointly learn the low-rank latent factors and the unknown noise variances from streaming data.
results: Numerical experiments show that SHASTA-PCA outperforms state-of-the-art streaming PCA algorithms in the heteroscedastic setting, and it is applied to highly-heterogeneous real data from astronomy.Here is the summary in Traditional Chinese:
results: 数据实验显示，SHASTA-PCA在不均势设定下超过现有的流动PCA算法，并应用于天文学中高度不均势的实际数据。Abstract
Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces of very high dimensional and high arrival-rate data with missing entries and corrupting noise. However, modern trends increasingly combine data from a variety of sources, meaning they may exhibit heterogeneous quality across samples. Since standard streaming PCA algorithms do not account for non-uniform noise, their subspace estimates can quickly degrade. On the other hand, the recently proposed Heteroscedastic Probabilistic PCA Technique (HePPCAT) addresses this heterogeneity, but it was not designed to handle missing entries and streaming data, nor does it adapt to non-stationary behavior in time series data. This paper proposes the Streaming HeteroscedASTic Algorithm for PCA (SHASTA-PCA) to bridge this divide. SHASTA-PCA employs a stochastic alternating expectation maximization approach that jointly learns the low-rank latent factors and the unknown noise variances from streaming data that may have missing entries and heteroscedastic noise, all while maintaining a low memory and computational footprint. Numerical experiments validate the superior subspace estimation of our method compared to state-of-the-art streaming PCA algorithms in the heteroscedastic setting. Finally, we illustrate SHASTA-PCA applied to highly-heterogeneous real data from astronomy.
摘要
流动主成分分析（PCA）是大规模机器学习中不可或缺的工具，用于快速估计高维数据中的低维子空间，该数据可能含有缺失项和噪声。然而，当现代趋势尝试将不同来源的数据组合起来时，这些数据可能会具有不同的质量水平。标准的流动PCA算法不会考虑非均匀噪声，因此其子空间估计可能很快地下降。相反，最近提出的随机概率PCA技术（HePPCAT）可以处理这种不同质量的数据，但它没有考虑流动数据和缺失项。这篇论文提出了流动随机预期最大化算法（SHASTA-PCA），用于融合这些因素。 SHASTA-PCA使用了随机 alternate expectation maximization方法，同时学习流动数据中缺失项和不同质量噪声的低维 latent factor和未知噪声方差，并保持低的内存和计算负担。数学实验表明，我们的方法在不同质量的噪声情况下比state-of-the-art streaming PCA算法更好地估计子空间。最后，我们示例了 SHASTA-PCA 应用于天文学中的高度不同质量数据。
Multiscale information fusion for fault detection and localization of battery energy storage systems
results: 实验结果表明，提出的多尺度检测指标可以快速和准确地检测锂离子电池系统中的短路异常，并且可以准确地定位异常的电池单元。Abstract
Battery energy storage system (BESS) has great potential to combat global warming. However, internal abnormalities in the BESS may develop into thermal runaway, causing serious safety incidents. In this study, the multiscale information fusion is proposed for thermal abnormality detection and localization in BESSs. We introduce the concept of dissimilarity entropy as a means to identify anomalies for lumped variables, whereas spatial and temporal entropy measures are presented for the detection of anomalies for distributed variables. Through appropriate parameter optimization, these three entropy functions are integrated into the comprehensive multiscale detection index, which outperforms traditional single-scale detection methods. The proposed multiscale statistic has good interpretability in terms of system energy concentration. Battery system internal short circuit (ISC) experiments have demonstrated that our proposed method can swiftly identify ISC abnormalities and accurately pinpoint the problematic battery cells.
摘要
锂离子电池能量存储系统（BESS）具有潜在的气候变化防控潜力。然而，BESS中的内部异常可能会导致热跑away，引起严重的安全事件。本研究提议了多级信息融合，用于探测和定位BESS中的热异常。我们引入了异常指标基于杂合变量的异同熵概念，而空间和时间异同熵度量则用于探测分布变量上的热异常。通过合适的参数优化，这三种异同熵函数被集成为了全面的多级检测指标，超越了传统单级检测方法。提议的多级统计具有系统能量集中的良好解释性。锂离子电池内部短路（ISC）实验表明，我们提议的方法可快速检测到ISC异常情况，并准确地确定问题的电池单元。
Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach
paper_authors: Yukun Cheng, Wei Chen, Lun Li, Bo Ai
for: 提高LDPC编码器的decoding性能
methods: 使用多任务学习和Structure of raptor-like LDPC codes
results: 可以处理多个代码速率，无需牺牲帧错误率性能Abstract
Deep learning based decoding networks have shown significant improvement in decoding LDPC codes, but the neural decoders are limited by rate-matching operations such as puncturing or extending, thus needing to train multiple decoders with different code rates for a variety of channel conditions. In this correspondence, we propose a Multi-Task Learning based rate-compatible LDPC ecoding network, which utilizes the structure of raptor-like LDPC codes and can deal with multiple code rates. In the proposed network, different portions of parameters are activated to deal with distinct code rates, which leads to parameter sharing among tasks. Numerical experiments demonstrate the effectiveness of the proposed method. Training the specially designed network under multiple code rates makes the decoder compatible with multiple code rates without sacrificing frame error rate performance.
摘要
深度学习基于解码网络已经显著提高了LDPC码解码性能，但神经解码器受限于比率匹配操作如抽割或扩展，因此需要训练多个解码器以适应不同的通道条件。在这封通信中，我们提出了基于多任务学习的Rate-Compatible LDPC编码网络，该网络利用了飞行鸟式LDPC码的结构，可以处理多个代码速率。在我们的提案中，不同的参数部分会在不同的代码速率下被激活，从而实现参数共享。数字实验证明我们的方法的有效性。通过特地设计的网络在多个代码速率下进行训练，使解码器与多个代码速率兼容无需牺牲帧错误率性能。