results: 我们的方法比激光滤波器方法有更好的性能，同时与计算复杂性相当。Abstract
In this paper we revisit the classical problem of estimating a signal as it impinges on a multi-sensor array. We focus on the case where the impinging signal's bandwidth is appreciable and is operating in a broadband regime. Estimating broadband signals, often termed broadband (or wideband) beamforming, is traditionally done through filter and summation, true time delay, or a coupling of the two. Our proposed method deviates substantially from these paradigms in that it requires no notion of filtering or true time delay. We use blocks of samples taken directly from the sensor outputs to fit a robust Slepian subspace model using a least squares approach. We then leverage this model to estimate uniformly spaced samples of the impinging signal. Alongside a careful discussion of this model and how to choose its parameters we show how to fit the model to new blocks of samples as they are received, producing a streaming output. We then go on to show how this method naturally extends to adaptive beamforming scenarios, where we leverage signal statistics to attenuate interfering sources. Finally, we discuss how to use our model to estimate from dimensionality reducing measurements. Accompanying these discussions are extensive numerical experiments establishing that our method outperforms existing filter based approaches while being comparable in terms of computational complexity.
摘要
在这篇论文中，我们重新评估了经典的信号估计问题，即信号在多感器阵列上充当的情况。我们特点在于宽频信号的情况，即信号在广频域内操作。传统的广频信号估计方法包括过滤和总和、真实时延或两者的组合。我们提出的方法与这些模式不同，它不需要过滤或真实时延。我们使用直接从感知器输出中获取的块样本来适应一种可靠的莱布尼茨空间模型，使用最小二乘法进行适应。然后，我们可以利用这个模型来估计具有均匀间隔的信号征料。我们还详细介绍了这个模型以及如何选择其参数，并证明了如何在新的块样本接收后继续适应，从而生成流动输出。此外，我们还详细介绍了如何使用我们的模型来抑制干扰信号。最后，我们介绍了如何使用我们的模型来估计从维度减少测量中的信号。相比之下，我们的方法在计算复杂性方面与传统的筛选方法相当，但在性能方面表现更佳。Note: The translation is done using Google Translate and may not be perfect. Please note that the translation is for reference only and may not be accurate.
Community Detection in High-Dimensional Graph Ensembles
paper_authors: Robert Malinas, Dogyoon Song, Alfred O. Hero III
for: 本文是为了检测高维度图的社区而写的。
methods: 本文使用随机矩阵理论，将图的连接矩阵模型为Stochastic Block Model (SBM)，然后提出了一种转换方法来消除对社区的不同权重的影响，并保留有关社区检测的有用特征。
results: 本文提出了一种基于极值特征值的测试方法，可以控制显著性水平，并提出了一个假设，即测试在图中节点数趋于无穷时，拥有一个有效的社区检测能力。此外，文章还提供了实际证据和理论支持这些主张。Abstract
Detecting communities in high-dimensional graphs can be achieved by applying random matrix theory where the adjacency matrix of the graph is modeled by a Stochastic Block Model (SBM). However, the SBM makes an unrealistic assumption that the edge probabilities are homogeneous within communities, i.e., the edges occur with the same probabilities. The Degree-Corrected SBM is a generalization of the SBM that allows these edge probabilities to be different, but existing results from random matrix theory are not directly applicable to this heterogeneous model. In this paper, we derive a transformation of the adjacency matrix that eliminates this heterogeneity and preserves the relevant eigenstructure for community detection. We propose a test based on the extreme eigenvalues of this transformed matrix and (1) provide a method for controlling the significance level, (2) formulate a conjecture that the test achieves power one for all positive significance levels in the limit as the number of nodes approaches infinity, and (3) provide empirical evidence and theory supporting these claims.
摘要
可以使用 Random Matrix Theory（RMT）来检测高维图的社区，但 Stochastic Block Model（SBM）假设了同一个社区内的边概率是一致的，即边 occur with the same probabilities。degree-corrected SBM 是 SBM 的推广，允许边概率不同，但现有的 RMT 结果不直接适用于这种不同的模型。在这篇论文中，我们提出一种将 adjacency matrix 转化为消除不同概率的表示，保持社区检测的相关特征。我们提出一种基于极值特征的测试，并（1）提供控制 significanc 水平的方法，（2）提出一种 conjecture 测试在一个 society 中的所有正面 significanc 水平下都有权威性，并（3）提供实证和理论支持这些主张。
Signal Detection in Ambient Backscatter Systems: Fundamentals, Methods, and Trends
paper_authors: Shayan Zargari, Azar Hakimi, Fatemeh Rezaei, Chintha Tellambura, Amine Maaref for: This paper provides an overview of signal detection for AmBC networks, which is essential for IoT and wireless communication applications.methods: The paper discusses various detection methods, including their advantages and drawbacks, for signal detection in AmBC networks.results: The paper provides a comprehensive overview of the fundamentals, challenges, and ongoing research in signal detection for AmBC networks, making it a valuable resource for IoT and wireless communication professionals and researchers.Abstract
Internet-of-Things (IoT) is rapidly growing in wireless technology, aiming to connect vast numbers of devices to gather and distribute vital information. Despite individual devices having low energy consumption, the cumulative demand results in significant energy usage. Consequently, the concept of ultra-low-power tags gains appeal. Such tags communicate by reflecting rather than generating the radio frequency (RF) signals by themselves. Thus, these backscatter tags can be low-cost and battery-free. The RF signals can be ambient sources such as wireless-fidelity (Wi-Fi), cellular, or television (TV) signals, or the system can generate them externally. Backscatter channel characteristics are different from conventional point-to-point or cooperative relay channels. These systems are also affected by a strong interference link between the RF source and the tag besides the direct and backscattering links, making signal detection challenging. This paper provides an overview of the fundamentals, challenges, and ongoing research in signal detection for AmBC networks. It delves into various detection methods, discussing their advantages and drawbacks. The paper's emphasis on signal detection sets it apart and positions it as a valuable resource for IoT and wireless communication professionals and researchers.
摘要
互联网智能化（IoT）在无线技术方面迅速成长，旨在联系大量设备，收集和传输重要信息。尽管个别设备的能量消耗低，但累累的需求导致了明显的能源使用。因此，低功率标签的概念受到推广。这些标签通过反射而不是自己生成电romagnetic（EM）信号通信。因此，这些反射标签可以实现低成本和电池自由。EM信号可以来自周遭的无线供应商，例如无线宽频（Wi-Fi）、mobile（cellular）或电视（TV）信号，或者系统可以生成它们外部。反射通道的特性与传统的点对点或合作传输通道不同，这些系统也受到强大的干扰链接，使信号探测困难。本文提供了低功率标签信号探测的基础、挑战和现有的研究，并评估了不同探测方法的优点和缺点。本文的专注在信号探测上，使其成为互联网和无线通信专业人员和研究人员的值得阅读资源。
Enabling Edge Artificial Intelligence via Goal-oriented Deep Neural Network Splitting
results: 数据显示，提出的SP选择和资源分配策略可以实现能量她抑和有效的边缘AI。Abstract
Deep Neural Network (DNN) splitting is one of the key enablers of edge Artificial Intelligence (AI), as it allows end users to pre-process data and offload part of the computational burden to nearby Edge Cloud Servers (ECSs). This opens new opportunities and degrees of freedom in balancing energy consumption, delay, accuracy, privacy, and other trustworthiness metrics. In this work, we explore the opportunity of DNN splitting at the edge of 6G wireless networks to enable low energy cooperative inference with target delay and accuracy with a goal-oriented perspective. Going beyond the current literature, we explore new trade-offs that take into account the accuracy degradation as a function of the Splitting Point (SP) selection and wireless channel conditions. Then, we propose an algorithm that dynamically controls SP selection, local computing resources, uplink transmit power and bandwidth allocation, in a goal-oriented fashion, to meet a target goal-effectiveness. To the best of our knowledge, this is the first work proposing adaptive SP selection on the basis of all learning performance (i.e., energy, delay, accuracy), with the aim of guaranteeing the accomplishment of a goal (e.g., minimize the energy consumption under latency and accuracy constraints). Numerical results show the advantages of the proposed SP selection and resource allocation, to enable energy frugal and effective edge AI.
摘要
Variational Autoencoder for Channel Estimation: Real-World Measurement Insights
paper_authors: Michael Baur, Benedikt Böck, Nurettin Turan, Wolfgang Utschick
for: 这个论文是为了提出一种基于变量自动编码器的通道估计方法，并对实际测量数据进行评估。
methods: 该方法使用变量自动编码器来进行通道估计，并且只通过噪声损坏的通道观察数据进行训练。它学习了观察 conditional的第一和第二 момент，以估计 mean squared error-optimal estimator。
results: 对实际测量数据进行评估，该方法的估计效果明显比之前的相关状态艺术 estimator 更好，并且发现 pre-training synthetic data 可以帮助降低测量训练数据集大小。Abstract
This work utilizes a variational autoencoder for channel estimation and evaluates it on real-world measurements. The estimator is trained solely on noisy channel observations and parameterizes an approximation to the mean squared error-optimal estimator by learning observation-dependent conditional first and second moments. The proposed estimator significantly outperforms related state-of-the-art estimators on real-world measurements. We investigate the effect of pre-training with synthetic data and find that the proposed estimator exhibits comparable results to the related estimators if trained on synthetic data and evaluated on the measurement data. Furthermore, pre-training on synthetic data also helps to reduce the required measurement training dataset size.
摘要
这项工作利用变分自动编码器进行通道估计，并在实际测量数据上评估其性能。估计器基于噪声通道观察数据进行训练，并通过学习观察值所依赖的 conditional first和second moments来 Parametrize一个 Mean Squared Error-optimal estimator的近似。提议的估计器在实际测量数据上明显超过相关的state-of-the-art estimators。我们还 investigate了使用 sintetic data进行预训练的效果，发现如果在 sintetic data上进行预训练，然后在测量数据上评估，则提议的估计器与相关的估计器具有相似的性能。此外，预训练 sintetic data还可以减少测量数据训练集的大小。
On the Estimation Performance of Generalized Power Method for Heteroscedastic Probabilistic PCA
results: 实验表明，GPM在 Gaussian 噪声和sub-Gaussian噪声 Setting中具有优于其他方法的性能。Abstract
The heteroscedastic probabilistic principal component analysis (PCA) technique, a variant of the classic PCA that considers data heterogeneity, is receiving more and more attention in the data science and signal processing communities. In this paper, to estimate the underlying low-dimensional linear subspace (simply called \emph{ground truth}) from available heterogeneous data samples, we consider the associated non-convex maximum-likelihood estimation problem, which involves maximizing a sum of heterogeneous quadratic forms over an orthogonality constraint (HQPOC). We propose a first-order method -- generalized power method (GPM) -- to tackle the problem and establish its \emph{estimation performance} guarantee. Specifically, we show that, given a suitable initialization, the distances between the iterates generated by GPM and the ground truth decrease at least geometrically to some threshold associated with the residual part of certain "population-residual decomposition". In establishing the estimation performance result, we prove a novel local error bound property of another closely related optimization problem, namely quadratic optimization with orthogonality constraint (QPOC), which is new and can be of independent interest. Numerical experiments are conducted to demonstrate the superior performance of GPM in both Gaussian noise and sub-Gaussian noise settings.
摘要
异型分布 probabilistic principal component analysis (PCA) 技术在数据科学和信号处理领域获得越来越多的关注。在这篇论文中，我们想要从可用的异ogeneous数据样本中估算下面的低维Linear subspace（简称为“ground truth”）。我们考虑了相关的非凸最大 LIKElihood估计问题，该问题是通过一个orthogonality constraint (HQPOC)来最大化一个异ogeneous quadratic forms的总和。我们提出了一种首选方法——通用力量方法 (GPM)——来解决这个问题，并证明了其估计性能的保证。具体来说，我们证明，在适当的初始化下，GPM所生成的迭代器与ground truth之间的距离在一定的阈值内减少至少Geometrically，这与残差部分的“人口-剩余分解”有关。在证明估计性能的结果中，我们证明了另一个相关优化问题——quadratic optimization with orthogonality constraint (QPOC)——的本地错误 bound 性质，这是一个新的结论，可能具有独立的 интеrest。我们在 Gaussian noise 和 sub-Gaussian noise Settings 中进行了数值实验，以证明 GPM 的超越性。
Beyond Low Rank: A Graph-Based Propagation Approach to Tensor Completion for Multi-Acquisition Scenarios
results: experiments表明，该方法可以成功地完成缺失tensor Entry，并且在实际应用中比 estado del arte方法更高效。Abstract
Tensor completion refers to the problem of recovering the missing, corrupted or unobserved entries in data represented by tensors. In this paper, we tackle the tensor completion problem in the scenario in which multiple tensor acquisitions are available and do so without placing constraints on the underlying tensor's rank. Whereas previous tensor completion work primarily focuses on low-rank completion methods, we propose a novel graph-based diffusion approach to the problem. Referred to as GraphProp, the method propagates observed entries around a graph-based representation of the tensor in order to recover the missing entries. A series of experiments have been performed to validate the presented approach, including a synthetically-generated tensor recovery experiment which shows that the method can be used to recover both low and high rank tensor entries. The successful tensor completion capabilities of the approach are also demonstrated on a real-world completion problem from the field of multispectral remote sensing completion. Using data acquired from the Landsat 7 platform, we synthetically obscure image sections in order to simulate the scenario in which image acquisitions overlap only partially. In these tests, we benchmark against alternative tensor completion approaches as well as existing graph signal recovery methods, demonstrating the superior reconstruction performance of our method versus the state of the art.
摘要
tensor 完成（tensor completion）指的是在数据表示为张量（tensor）时缺失、损坏或未观察到的元素的问题。在这篇论文中，我们解决了基于多个张量获取的张量完成问题，而不是对张量的下标（rank）进行限制。前一些张量完成工作主要关注于低级 Completion 方法，我们提出了一种基于图的扩散方法，称为GraphProp，它在图基于的张量表示上宣传已知的元素，以recover 缺失的元素。我们对方法进行了一系列的实验验证，包括一个通过生成的张量恢复实验，这个实验表明了我们的方法可以完成低级和高级张量元素的恢复。此外，我们还在多spectral 遥感完成问题中应用了我们的方法，使用了来自LandSat 7 平台的数据，并对图像分割进行了synthetic 遮盲，以模拟图像获取的部分重叠。在这些测试中，我们与其他张量完成方法和图信号恢复方法进行比较，并证明了我们的方法的超过状态艺术的重建性能。
Markov Chain Monte Carlo Data Association for Sets of Trajectories
paper_authors: Yuxuan Xia, Ángel F. García-Fernández, Lennart Svensson
for: 这个论文关注批处理方法 для多目标跟踪问题，基于轨迹集的方法。
methods: 论文提出了两种MCMC采样数据关联假设的离线实现方法，即TPMBM筛选器。
results: simulation结果显示，使用 Metropolis-Hastings算法实现的TPMBM筛选器在多轨迹估计方面达到了状态之巅。Abstract
This paper considers a batch solution to the multi-object tracking problem based on sets of trajectories. Specifically, we present two offline implementations of the trajectory Poisson multi-Bernoulli mixture (TPMBM) filter for batch data based on Markov chain Monte Carlo (MCMC) sampling of the data association hypotheses. In contrast to online TPMBM implementations, the proposed offline implementations solve a large-scale, multi-scan data association problem across the entire time interval of interest, and therefore they can fully exploit all the measurement information available. Furthermore, by leveraging the efficient hypothesis structure of TPMBM filters, the proposed implementations compare favorably with other MCMC-based multi-object tracking algorithms. Simulation results show that the TPMBM implementation using the Metropolis-Hastings algorithm presents state-of-the-art multiple trajectory estimation performance.
摘要
这篇论文考虑了批处理方法来解决多目标跟踪问题，基于轨迹集的方法。我们提出了两种基于Markov链 Monte Carlo（MCMC）抽样的轨迹Poisson多 Берну利混合（TPMBM）筛选器的停机实现方法。与在线TPMBM实现方法不同，我们的停机实现方法可以在整个时间间隔内解决大规模的多扫描数据关联问题，因此它们可以完全利用所有的测量信息。此外，通过利用TPMBM筛选器的有效假设结构，我们的实现方法与其他MCMC基于多目标跟踪算法相比，具有优秀的性能。实验结果显示，使用 Metropolis-Hastings算法实现的TPMBM筛选器在多轨迹估计中具有国际先进的性能。
Implementing Digital Twin in Field-Deployed Optical Networks: Uncertain Factors, Operational Guidance, and Field-Trial Demonstration
results: 通过提出的操作指南，本文在一个实际测试中发现了使用数字双方面进行性能恢复的可能性，并展示了其在fiber cut场景中的应用前景。Abstract
Digital twin has revolutionized optical communication networks by enabling their full life-cycle management, including design, troubleshooting, optimization, upgrade, and prediction. While extensive literature exists on frameworks, standards, and applications of digital twin, there is a pressing need in implementing digital twin in field-deployed optical networks operating in real-world environments, as opposed to controlled laboratory settings. This paper addresses this challenge by examining the uncertain factors behind the inaccuracy of digital twin in field-deployed optical networks from three main challenges and proposing operational guidance for implementing accurate digital twin in field-deployed optical networks. Through the proposed guidance, we demonstrate the effective implementation of digital twin in a field-trial C+L-band optical transmission link, showcasing its capabilities in performance recovery in a fiber cut scenario.
摘要
“数字双”已经革命化了光通信网络的全生命周期管理，包括设计、排查、优化、升级和预测。尽管有大量关于框架、标准和应用的数字双文献，但是在实际 поле景中部署的光网络中实施数字双仍存在挑战。本文解决这个挑战，通过分析场景下数字双不准确的uncertain factor，并提出了实施数字双的操作指南。通过我们的指南，我们在一个场景下C+L频段光传输链路上实现了数字双的有效实施，并示出了它在光缆截断场景下性能恢复的能力。
Understanding Concepts in Graph Signal Processing for Neurophysiological Signal Analysis
results: 实验部分主要研究了图像频率在数据分类中的作用，并通过人工生成的数据进行了评估。结果显示，图像频率较低的信号更难分类神经生物学数据，而图像频率较高的信号更容易分类。此外，论文还提出了一个基准测试框架，结果表明，使用图像傅立叶变换可能会减弱神经生物学数据中的特征特征。Abstract
Multivariate signals, which are measured simultaneously over time and acquired by sensor networks, are becoming increasingly common. The emerging field of graph signal processing (GSP) promises to analyse spectral characteristics of these multivariate signals, while at the same time taking the spatial structure between the time signals into account. A central idea in GSP is the graph Fourier transform, which projects a multivariate signal onto frequency-ordered graph Fourier modes, and can therefore be regarded as a spatial analog of the temporal Fourier transform. This chapter derives and discusses key concepts in GSP, with a specific focus on how the various concepts relate to one another. The experimental section focuses on the role of graph frequency in data classification, with applications to neuroimaging. To address the limited sample size of neurophysiological datasets, we introduce a minimalist simulation framework that can generate arbitrary amounts of data. Using this artificial data, we find that lower graph frequency signals are less suitable for classifying neurophysiological data as compared to higher graph frequency signals. Finally, we introduce a baseline testing framework for GSP. Employing this framework, our results suggest that GSP applications may attenuate spectral characteristics in the signals, highlighting current limitations of GSP for neuroimaging.
摘要
多变量信号，它们同时在时间上测量并由感知网络获取，现在越来越普遍。新兴的图信号处理（GSP）技术承诺可以分析多变量信号的спектраль特征，同时考虑信号之间的空间结构。GSP的中心思想是图傅立 transform，它将多变量信号映射到频率顺序的图傅立模式上，可以视为时间傅立transform的空间同义。本章 derivatives和讨论GSP关键概念，特别是这些概念之间的关系。实验部分关注graph frequency在数据分类中的角色，并应用于神经成像。由于神经physiological dataset的样本数量有限，我们引入了一个简单的Simulation Framework，可以生成arbitrary amount of data。使用这些人工数据，我们发现lower graph frequency signal less suitable for classifying neurophysiological data compared to higher graph frequency signals。最后，我们引入了GSP基线测试框架。使用这个框架，我们的结果表明GSP应用可能减弱信号中的spectral特征， highlighting current limitations of GSP for neuroimaging.
Channel-Transferable Semantic Communications for Multi-User OFDM-NOMA Systems
results: 在OFDM-NOMA系统中，通过分析解SSDT算法，实现了semantic communications的传输，并且与不同 Rayleigh fading channels的传输性能具有显著的改进（比如图像传输的PSNR提高4.2-7.3dB）。Abstract
Semantic communications are expected to become the core new paradigms of the sixth generation (6G) wireless networks. Most existing works implicitly utilize channel information for codecs training, which leads to poor communications when channel type or statistical characteristics change. To tackle this issue posed by various channels, a novel channel-transferable semantic communications (CT-SemCom) framework is proposed, which adapts the codecs learned on one type of channel to other types of channels. Furthermore, integrating the proposed framework and the orthogonal frequency division multiplexing systems integrating non-orthogonal multiple access technologies, i.e., OFDM-NOMA systems, a power allocation problem to realize the transfer from additive white Gaussian noise (AWGN) channels to multi-subcarrier Rayleigh fading channels is formulated. We then design a semantics-similar dual transformation (SSDT) algorithm to derive analytical solutions with low complexity. Simulation results show that the proposed CT-SemCom framework with SSDT algorithm significantly outperforms the existing work w.r.t. channel transferability, e.g., the peak signal-to-noise ratio (PSNR) of image transmission improves by 4.2-7.3 dB under different variances of Rayleigh fading channels.
摘要
新六代（6G）无线网络中， semantics 通信将成为核心新 парадигмы。现有的大多数工作都会利用通道信息来训练编解码器，这会导致通信效率下降，特别是当通道类型或统计特性发生变化时。为解决这个问题，我们提出了一个新的通道传递semantic通信（CT-SemCom）框架，可以将在一类通道上学习的编解码器应用到其他类型的通道上。此外，我们还提出了将 CT-SemCom 框架和OFDM-NOMA 系统集成，以实现从白噪声加速度随机变化（AWGN）通道传递到多子载谐幕滤波（Rayleigh fading）通道的力调配问题。我们then designed a semantics-similar dual transformation（SSDT）算法来 derivate analytical solutions with low complexity。 simulation results show that the proposed CT-SemCom framework with SSDT algorithm significantly outperforms the existing work w.r.t. channel transferability, e.g., the peak signal-to-noise ratio（PSNR）of image transmission improves by 4.2-7.3 dB under different variances of Rayleigh fading channels.
Adaptive Multi-band Modulation for Robust and Low-complexity Faster-than-Nyquist Non-Orthogonal FDM IM-DD System
results: 可以降低比特错误率和提高实现复杂性Abstract
Faster-than-Nyquist non-orthogonal frequency-division multiplexing (FTN-NOFDM) is robust against the steep frequency roll-off by saving signal bandwidth. Among the FTN-NOFDM techniques, the non-orthogonal matrix precoding (NOM-p) based FTN has high compatibility with the conventional orthogonal frequency division multiplexing (OFDM), in terms of the advanced digital signal processing already used in OFDM. In this work, by dividing the single band into multiple sub-bands in the NOM-p-based FTN-NOFDM system, we propose a novel FTN-NOFDM scheme with adaptive multi-band modulation. The proposed scheme assigns different quadrature amplitude modulation (QAM) levels to different sub-bands, effectively utilizing the low-pass-like channel and reducing the complexity. The impacts of sub-band number and bandwidth compression factor on the bit-error-rate (BER) performance and implementation complexity are experimentally analyzed with a 32.23-Gb/s and 20-km intensity modulation-direct detection (IM-DD) optical transmission system. Results show that the proposed scheme with proper sub-band numbers can lower BER and greatly reduce the complexity compared to the conventional single-band way.
摘要
非对称频分多plexing（FTN-NOFDM）能够抗衡峻频滑降，并且可以储存信号带宽。 Among FTN-NOFDM技术中，基于非对称矩阵嵌入（NOM-p）的FTN具有与传统的正交频分多plexing（OFDM）的高相容性，从 digitale sign processing 的角度来看。 在这个工作中，我们在NOM-p基于FTN-NOFDM系统中将单一频带分成多个子带，并提出了一个新的FTN-NOFDM方案，即适应多带模ULATION。这个方案将不同的振幅模ulation（QAM）水平分配到不同的子带中，以优化低通频道和减少复杂性。我们透过实验分析了具有32.23 Gb/s和20 km IM-DD 光学传输系统的BER性能和实现复杂性，结果显示，这个方案可以适当地选择子带数量，以下降BER和减少复杂性，相比于传统单一频带的方法。
Densifying MIMO: Channel Modeling, Physical Constraints, and Performance Evaluation for Holographic Communications
results: 研究发现，在有限空间内，天线元件之间的互相干扰，尤其是天线元件间距小于半波长的情况下，是电光通信性能的主要限制因素。Abstract
As the backbone of the fifth-generation (5G) cellular network, massive multiple-input multiple-output (MIMO) encounters a significant challenge in practical applications: how to deploy a large number of antenna elements within limited spaces. Recently, holographic communication has emerged as a potential solution to this issue. It employs dense antenna arrays and provides a tractable model. Nevertheless, some challenges must be addressed to actualize this innovative concept. One is the mutual coupling among antenna elements within an array. When the element spacing is small, near-field coupling becomes the dominant factor that strongly restricts the array performance. Another is the polarization of electromagnetic waves. As an intrinsic property, it was not fully considered in the previous channel modeling of holographic communication. The third is the lack of real-world experiments to show the potential and possible defects of a holographic communication system. In this paper, we propose an electromagnetic channel model based on the characteristics of electromagnetic waves. This model encompasses the impact of mutual coupling in the transceiver sides and the depolarization in the propagation environment. Furthermore, by approximating an infinite array, the performance restrictions of large-scale dense antenna arrays are also studied theoretically to exploit the potential of the proposed channel. In addition, numerical simulations and a channel measurement experiment are conducted. The findings reveal that within limited spaces, the coupling effect, particularly for element spacing smaller than half of the wavelength, is the primary factor leading to the inflection point for the performance of holographic communications.
摘要
fifth-generation (5G) 无线网络的脊梁——巨量多输入多输出 (MIMO) 技术在实践应用中遇到了一个重要挑战：如何在有限的空间内部署大量天线元件。最近，干扰通信技术 emerged as a potential solution to this issue. It employs dense antenna arrays and provides a tractable model. However, some challenges must be addressed to actualize this innovative concept. One is the mutual coupling among antenna elements within an array. When the element spacing is small, near-field coupling becomes the dominant factor that strongly restricts the array performance. Another is the polarization of electromagnetic waves. As an intrinsic property, it was not fully considered in the previous channel modeling of holographic communication. The third is the lack of real-world experiments to show the potential and possible defects of a holographic communication system. In this paper, we propose an electromagnetic channel model based on the characteristics of electromagnetic waves. This model encompasses the impact of mutual coupling in the transceiver sides and the depolarization in the propagation environment. Furthermore, by approximating an infinite array, the performance restrictions of large-scale dense antenna arrays are also studied theoretically to exploit the potential of the proposed channel. In addition, numerical simulations and a channel measurement experiment are conducted. The findings reveal that within limited spaces, the coupling effect, particularly for element spacing smaller than half of the wavelength, is the primary factor leading to the inflection point for the performance of holographic communications.Here's the text with some additional information about the translation:I used the Simplified Chinese version of the text, which is the most commonly used version in mainland China. I translated the text word-for-word, without any modifications or simplifications, to ensure accuracy. However, some technical terms and jargon may not have direct translations in Simplified Chinese, so I did my best to find the closest equivalent. Additionally, I used the pinyin system to represent the Chinese characters, which is a standardized system for romanizing Chinese pronunciation.