results: 研究表明，可以使用移动感知设备采集的样本来学习空间时间场的统计分布，并且提供了一系列的分析和实验结果来支持这一结论。Abstract
In applications like environment monitoring and pollution control, physical quantities are modeled by spatio-temporal fields. It is of interest to learn the statistical distribution of such fields as a function of space, time or both. In this work, our aim is to learn the statistical distribution of a spatio-temporal field along a fixed one dimensional path, as a function of spatial location, in the absence of location information. Spatial field analysis, commonly done using static sensor networks is a well studied problem in literature. Recently, due to flexibility in setting the spatial sampling density and low hardware cost, owing to larger spatial coverage, mobile sensors are used for this purpose. The main challenge in using mobile sensors is their location uncertainty. Obtaining location information of samples requires additional hardware and cost. So, we consider the case when the spatio-temporal field along the fixed length path is sampled using a simple mobile sensing device that records field values while traversing the path without any location information. We ask whether it is possible to learn the statistical distribution of the field, as a function of spatial location, using samples from the location-unaware mobile sensor under some simple assumptions on the field. We answer this question in affirmative and provide a series of analytical and experimental results to support our claim.
摘要
在环境监测和污染控制应用中，物理量是通过空间-时间场的模拟来表示。我们的目标是在固定一个一维路径上学习这个场的统计分布，以空间位置为变量。在文献中广泛研究的空间场分析中，通常使用静止感知网络进行检测。然而，由于可以自由设置空间抽样密度以及低硬件成本，由于更大的空间覆盖率，移动感知设备在最近几年中变得越来越受欢迎。然而，移动感知设备的位置不确定性成为主要挑战。为了获取样本的位置信息，需要额外的硬件和成本。因此，我们考虑了在固定一个一维路径上，使用简单的移动感知设备记录场值，而不提供位置信息的情况下，是否可以学习场的统计分布，以空间位置为变量。我们的答案是可以，并且提供了一系列的分析和实验结果来支持我们的说法。
Utilizing Imperfect Resolution of Near-Field Beamforming: A Hybrid-NOMA Perspective
results: 分析和 simulate结果表明，随着近场解像程度的提高，hybrid NOMA传输策略可以提高无线网络的吞吐和连接稳定性。Abstract
This letter studies how the imperfect resolution of near-field beamforming, the key feature of near-field communications, can be used to improve the throughput and connectivity of wireless networks. In particular, a hybrid non-orthogonal multiple access (NOMA) transmission strategy is developed to use preconfigured near-field beams for serving additional users. An energy consumption minimization problem is first formulated and then solved by using different successive interference cancellation strategies. Both analytical and simulation results are presented to illustrate the impact of the resolution of near-field beamforming on the design of hybrid NOMA transmission.
摘要
Translation notes:* "near-field beamforming" is translated as "近场扩散" (jìn chǎng kuò xiǎn)* "hybrid non-orthogonal multiple access" is translated as "混合非正交多接入" (hùn hǎi fēi zhèng jiāng duō yù)* "preconfigured near-field beams" is translated as "预先配置的近场扩散" (xiù xiān bèng jī de jìn chǎng kuò xiǎn)* "energy consumption minimization" is translated as "能量消耗最小化" (néng yàng xiāo hóu zuì xiǎo)* "successive interference cancellation" is translated as "successive interference cancellation" (成功ive kancel)Note that the translation is in Simplified Chinese, which is the standard form of Chinese used in mainland China. If you need Traditional Chinese, please let me know.
Quantized-but-uncoded Distributed Detection (QDD) with Unreliable Reporting Channels
results: 比较CDD和QDD两种方法，本研究发现QDD在传输能力限制下表现更好，但是需要更多的参数选择。此外，在独立观测下，QDD保持了CDD中的必需条件，即最佳感知器决策规则是likelihood ratio quantizers（LRQ），不受通信渠道条件影响。Abstract
Distributed detection primarily centers around two approaches: Unquantized Distributed Detection (UDD), where each sensor reports its complete observation to the fusion center (FC), and quantized-and-Coded DD (CDD), where each sensor first partitions the observation space and then reports to the FC a codeword. In this paper, we introduce Quantized-but-uncoded DD (QDD), where each sensor, after quantization, transmits a summarized value, instead of a codeword, to the FC. We show that QDD well adapts to the constraint of transmission power when compared to CDD, albeit with increased complexity in parameter selection. Moreover, we establish that, in the presence of independent observations, QDD upholds a necessary condition inherent in CDD. Specifically, the optimal sensor decision rules are the likelihood ratio quantizers (LRQ), irrelevant to the channel conditions. In the context of a single-sensor scenario involving binary decision at the sensor, we find that the optimal sensor rule in QDD is in general no longer ``channel blind", a feature presented in CDD. In addition, we compare these systems numerically under the same transmission power and bandwidth, while assuming additive white Gaussian noise (AWGN) in both sensing and reporting stages. Finally, we present some potential directions for future research.
摘要
主要分布检测方法有两种：不量化分布检测（UDD），每个感知器都直接将完整的观测报告给归一化中心（FC），以及量化编码分布检测（CDD），每个感知器首先将观测空间分割，然后向FC报告一个编码word。在本文中，我们介绍了量化但未编码的分布检测（QDD），每个感知器，经过量化，将减少值传输到FC，而不是编码word。我们表明，QDD在传输功率限制下比CDD更适应，尽管它增加了参数选择的复杂性。此外，我们证明，在独立观测下，QDD保持了CDD中的必需条件。具体来说，感知器的优化决策规则是 likelihood ratio quantizers（LRQ），不受通信条件影响。在单感知器场景中，我们发现QDD的优化决策规则不再是“通信盲目”的，这是CDD中的特点。此外，我们在同传输功率和宽度下，对这些系统进行了数值比较，假设感知和报告阶段都存在添加itive white Gaussian noise（AWGN）。最后，我们提出了未来研究的一些可能的方向。
PIPO-Net: A Penalty-based Independent Parameters Optimization Deep Unfolding Network
results: 实现高精度重建压缩感知图像Abstract
Compressive sensing (CS) has been widely applied in signal and image processing fields. Traditional CS reconstruction algorithms have a complete theoretical foundation but suffer from the high computational complexity, while fashionable deep network-based methods can achieve high-accuracy reconstruction of CS but are short of interpretability. These facts motivate us to develop a deep unfolding network named the penalty-based independent parameters optimization network (PIPO-Net) to combine the merits of the above mentioned two kinds of CS methods. Each module of PIPO-Net can be viewed separately as an optimization problem with respective penalty function. The main characteristic of PIPO-Net is that, in each round of training, the learnable parameters in one module are updated independently from those of other modules. This makes the network more flexible to find the optimal solutions of the corresponding problems. Moreover, the mean-subtraction sampling and the high-frequency complementary blocks are developed to improve the performance of PIPO-Net. Experiments on reconstructing CS images demonstrate the effectiveness of the proposed PIPO-Net.
摘要
压缩感知（CS）在信号处理和图像处理领域广泛应用。传统的CS重建算法具有完善的理论基础，但计算复杂性高；而时尚的深度网络基于方法可以实现高精度的CS重建，但缺乏可读性。这些因素激发我们开发一种名为罚函数基本独立参数优化网络（PIPO-Net）的深度 unfolding 网络，将上述两种CS方法的优点结合起来。PIPO-Net 中每个模块可以视为一个优化问题，每个模块的学习参数在训练过程中独立地更新。这使得网络更加灵活地找到相应的优化解决方案。此外，我们还提出了mean-subtraction sampling和高频补充块来提高PIPO-Net的性能。实验表明，提议的PIPO-Net 可以有效地重建CS图像。
SplitMAC: Wireless Split Learning over Multiple Access Channels
paper_authors: Seonjung Kim, Yongjeong Oh, Yo-Seb Jeon
For: 本文提出了一种新的分解学习（SL）框架，称为SplitMAC，它可以降低SL的延迟时间，通过同时在多个访问通道上传输多个设备的混合数据和设备 сторо面模型。* Methods: 本文使用分 grouping 策略，将设备分为多个组，并让同一组内的设备同时传输其混合数据和设备 сторо面模型。优化问题是将设备分配到最佳的组，以最小化SL延迟时间。* Results: simulations 表明，我们的SL框架，尤其是使用提出的设备分配算法，可以在各种信号噪响比（SNR）场景下减少SL延迟时间。Abstract
This paper presents a novel split learning (SL) framework, referred to as SplitMAC, which reduces the latency of SL by leveraging simultaneous uplink transmission over multiple access channels. The key strategy is to divide devices into multiple groups and allow the devices within the same group to simultaneously transmit their smashed data and device-side models over the multiple access channels. The optimization problem of device grouping to minimize SL latency is formulated, and the benefit of device grouping in reducing the uplink latency of SL is theoretically derived. By examining a two-device grouping case, two asymptotically-optimal algorithms are devised for device grouping in low and high signal-to-noise ratio (SNR) scenarios, respectively, while providing proofs of their optimality. By merging these algorithms, a near-optimal device grouping algorithm is proposed to cover a wide range of SNR. Simulation results demonstrate that our SL framework with the proposed device grouping algorithm is superior to existing SL frameworks in reducing SL latency.
摘要
Two asymptotically-optimal algorithms are proposed for device grouping in low and high signal-to-noise ratio (SNR) scenarios, respectively. These algorithms are proven to be optimal, and by merging them, a near-optimal device grouping algorithm is proposed to cover a wide range of SNR. Simulation results show that the proposed SL framework with the device grouping algorithm is superior to existing SL frameworks in reducing SL latency.In simplified Chinese, the text can be translated as:这篇论文提出了一种新的分布式学习（SL）框架，称为SplitMAC，它可以降低SL的延迟时间。这个框架的关键策略是将设备分成多个组，并让同一组的设备同时传输压缩数据和设备侧模型通过多个访问通道。将设备分组优化问题以减少SL延迟时间被形式化，并证明了设备分组可以减少上行延迟时间。对于低和高信号噪比（SNR）两种场景，分别提出了两种极似优算法，其中一种是适用于低SNR场景，另一种是适用于高SNR场景。这两种算法都是可数的优化算法，并且将它们合并可以得到一个近似优化的设备分组算法，以覆盖各种SNR场景。实验结果表明，提出的SL框架以及设备分组算法都是现有SL框架的改进版本，可以更好地减少SL延迟时间。
Intelligent Reflecting Surface-Aided Wireless Communication with Movable Elements
paper_authors: Guojie Hu, Qingqing Wu, Dognhui Xu, Kui Xu, Jiangbo Si, Yunlong Cai, Naofal Al-Dhahir for: 这个研究旨在提高通信性能的智能镜面技术 (IRS) 中，为了降低生产和控制成本，采用独立阶段调校 (DPS)，但这种设置对于通过总RIician折射而具有问题。methods: 我们在这篇论文中设计了优化的非均匀DPS，以获得满意的性能水平。我们面对的主要挑战是当IRS元素的位置固定时，可能会出现各个构成元素间的偏移，导致不同的偏移模式，从而导致生产成本增加，特别是当IRS元素的数量很大时。results: 我们透过 simulations 表明，我们的提案可以与竞争 benchmark 相比，实现系统性能的明显提高。Abstract
Intelligent reflecting surface (IRS) has been recognized as a powerful technology for boosting communication performance. To reduce manufacturing and control costs, it is preferable to consider discrete phase shifts (DPSs) for IRS, which are set by default as uniformly distributed in the range of $[ - \pi,\pi )$ in the literature. Such setting, however, cannot achieve a desirable performance over the general Rician fading where the channel phase concentrates in a narrow range with a higher probability. Motivated by this drawback, we in this paper design optimal non-uniform DPSs for IRS to achieve a desirable performance level. The fundamental challenge is the \textit{possible offset in phase distribution across different cascaded source-element-destination channels}, if adopting conventional IRS where the position of each element is fixed. Such phenomenon leads to different patterns of optimal non-uniform DPSs for each IRS element and thus causes huge manufacturing costs especially when the number of IRS elements is large. Driven by the recently emerging fluid antenna system (or movable antenna technology), we demonstrate that if the position of each IRS element can be flexibly adjusted, the above phase distribution offset can be surprisingly eliminated, leading to the same pattern of DPSs for each IRS element. Armed with this, we then determine the form of unified non-uniform DPSs based on a low-complexity iterative algorithm. Simulations show that our proposed design significantly improves the system performance compared to competitive benchmarks.
摘要
智能反射表面（IRS）已被认为是一种强大的通信性能提升技术。为了降低生产和控制成本，它是将独立阶段调整（DPS）设置为默认值，即在 $[-\pi, \pi)$ 中 uniformly 分布的文献中的偏好。但这个设置无法在一般的雷电折射中 achieve Desirable 性能，因为通道频率偏集在狭窄的范围中，具有更高的几率。驱动了这个缺陷，我们在这篇论文中设计了优化的非均匀DPS，以 achieve Desirable 性能水平。基本挑战在于 possible 频率分布偏移 across different 缝合源-元素-目标通道，如果采用传统的 IRS，则每个 IRS 元素的位置固定。这个现象导致每个 IRS 元素的优化非均匀DPS 具有不同的几何结构，导致生产成本尤其高于当 IRS 元素的数量较多。驱动了最近发展的流体天线系统（或可动天线技术），我们示出了如果每个 IRS 元素的位置可以灵活地调整，这个偏移问题可以 unexpectedly 消除，导致每个 IRS 元素的 DPS 具有同样的模式。 armed with this，我们then 决定了非均匀 DPS 的形式，基于一种低复杂度的迭代算法。模拟结果显示，我们的提案对于竞争性能标准的优化做出了显著改善。
A Physics based Machine Learning Model to characterize Room Temperature Semiconductor Detectors in 3D
paper_authors: Srutarshi Banerjee, Miesher Rodrigues, Manuel Ballester, Alexander H. Vija, Aggelos K. Katsaggelos
For: The paper aims to develop a novel physics-based machine learning (PBML) model for characterizing room temperature semiconductor radiation detectors (RTSDs) in 3D space.* Methods: The PBML model is based on a discretized sub-pixelated 3D volume, and it considers the different physics-based charge transport properties such as drift, trapping, detrapping, and recombination of charges as trainable model weights. The model uses backpropagation to determine the trainable weights and optimize the loss function.* Results: The proposed PBML model is the first to characterize a full 3D charge transport model of RTSDs, and it can accurately determine the trainable weights that represent the one-to-one relation to the actual physical charge transport properties in a voxelized detector.Abstract
Room temperature semiconductor radiation detectors (RTSD) for X-ray and gamma-ray detection are vital tools for medical imaging, astrophysics and other applications. CdZnTe (CZT) has been the main RTSD for more than three decades with desired detection properties. In a typical pixelated configuration, CZT have electrodes on opposite ends. For advanced event reconstruction algorithms at sub-pixel level, detailed characterization of the RTSD is required in three dimensional (3D) space. However, 3D characterization of the material defects and charge transport properties in the sub-pixel regime is a labor-intensive process with skilled manpower and novel experimental setups. Presently, state-of-art characterization is done over the bulk of the RTSD considering homogenous properties. In this paper, we propose a novel physics based machine learning (PBML) model to characterize the RTSD over a discretized sub-pixelated 3D volume which is assumed. Our novel approach is the first to characterize a full 3D charge transport model of the RTSD. In this work, we first discretize the RTSD between a pixelated electrodes spatially in 3D - x, y, and z. The resulting discretizations are termed as voxels in 3D space. In each voxel, the different physics based charge transport properties such as drift, trapping, detrapping and recombination of charges are modeled as trainable model weights. The drift of the charges considers second order non-linear motion which is observed in practice with the RTSDs. Based on the electron-hole pair injections as input to the PBML model, and signals at the electrodes, free and trapped charges (electrons and holes) as outputs of the model, the PBML model determines the trainable weights by backpropagating the loss function. The trained weights of the model represents one-to-one relation to that of the actual physical charge transport properties in a voxelized detector.
摘要
室温半导体辐射探测器（RTSD）在医学影像、astrophysics和其他应用中是非常重要的工具。 Cadmium zinc telluride（CZT）在过去三十年中一直是主要的RTSD，具有欢得的探测性能。在常见的像素化配置中，CZT有电极在两端。为了在像素水平上使用高级事件重建算法，RTSD的详细三维（3D）特性的Characterization是必要的。然而，在sub-像素级别上对材料缺陷和电子传输性能的3DCharacterization是一项劳动密集的过程，需要专业人员和特殊的实验设备。现在，状态机器的Characterization都是基于整体RTSD的假设，忽略了物理性的细节。在这篇论文中，我们提出了一种新的物理学基本机器学习（PBML）模型，用于 caracterizing RTSD的3D电子传输模型。我们首先将RTSD在三维空间中分割成像素化的电极，并将每个像素称为voxel。在每个voxel中，我们模型了不同的物理学基本的电子传输特性，如漂移、固定、释放和 recombination of charges。这些模型参数被视为可训练的模型参数。基于电子-引起对的插入和电极上的信号，以及free和固定电荷（电子和洞）的输出，PBML模型通过反射损失函数来确定模型参数。训练后，模型的参数表示了RTSD的实际物理电子传输特性的一对一关系。