results: 研究发现，尽管增加G值可以减少网络节点中含有不可靠包的比例，但是这些包的版本年龄增加，从而导致了新鲜度-可靠性贸易offs。数据支持这些发现。Abstract
We consider a system model with two sources, a reliable source and an unreliable source, who are responsible for disseminating updates regarding a process to an age-based gossip network of $n$ nodes. Nodes wish to have fresh information, however, they have preference for packets that originated at the reliable source and are willing to sacrifice their version age of information by up to $G$ versions to switch from an unreliable packet to a reliable packet. We study how this protocol impacts the prevalence of unreliable packets at nodes in the network and their version age. Using a stochastic hybrid system (SHS) framework, we formulate analytical equations to characterize two quantities: expected fraction of nodes with unreliable packets and expected version age of information at network nodes. We show that as $G$ increases, fewer nodes have unreliable packet, however, their version age increases as well, thereby inducing a freshness-reliability trade-off in the network. We present numerical results to support our findings.
摘要
我们考虑一个系统模型，其包含两个源，一个可靠的源和一个不可靠的源，他们负责将进程更新传递给一个年龄基于谣言网络中的 $n$ 个节点。节点希望有最新的信息，但他们偏好来自可靠源的包，并愿意为了更换到可靠包而丢弃自己的版本年龄信息，最多为 $G$ 个版本。我们研究这种协议如何影响网络节点上带有不可靠包的普遍性和版本年龄。使用随机混合系统（SHS）框架，我们编写了分析方程来描述两个量：网络节点上带有不可靠包的预期总数和网络节点上的版本年龄预期值。我们发现，当 $G$ 增加时，网络节点上带有不可靠包的数量减少，但这些包的版本年龄也增加，从而导致了一种新鲜度-可靠性贸易。我们提供数据支持我们的发现。
Comparison of model selection techniques for seafloor scattering statistics
results: 研究发现，使用数据驱动的方法可以更好地选择折射环境中像Pixel强度分布的统计模型，并且可以减少人类的干预。Abstract
In quantitative analysis of seafloor imagery, it is common to model the collection of individual pixel intensities scattered by the seafloor as a random variable with a given statistical distribution. There is a considerable literature on statistical models for seafloor scattering, mostly focused on areas with statistically homogeneous properties (i.e. exhibiting spatial stationarity). For more complex seafloors, the pixel intensity distribution is more appropriately modeled using a mixture of simple distributions. For very complex seafloors, fitting 3 or more mixture components makes physical sense, but the statistical model becomes much more complex in these cases. Therefore, picking the number of components of the mixture model is a decision that must be made, using a priori information, or using a data driven approach. However, this information is time consuming to collect, and depends on the skill and experience of the human. Therefore, a data-driven approach is advantageous to use, and is explored in this work. Criteria for choosing a model always need to balance the trade-off for the best fit for the data on the one hand and the model complexity on the other hand. In this work, we compare several statistical model selection criteria, e.g., the Bayesian information criterion. Examples are given for SAS data collected by an autonomous underwater vehicle in a rocky environment off the coast of Bergen, Norway using data from the HISAS-1032 synthetic aperture sonar system.
摘要
在海底图像量化分析中，常将每个像素强度散射到海底模型为随机变量，采用给定的统计分布。关于海底散射的统计模型有很大的文献，主要集中在统计homogeneous（即空间站ARY）的海底上。对于更复杂的海底，则更有理由使用多个简单分布的混合模型。对于非常复杂的海底，使用3个或更多的混合组件是物理意义上的，但统计模型在这些情况下变得非常复杂。因此，选择混合模型的组件数量是一个需要基于先验知识或数据驱动的决策。然而，收集这些信息的时间很长，取决于人员的技能和经验。因此，使用数据驱动的方法更有利，并在这种工作中进行了研究。选择模型的标准要求平衡数据最佳适应和模型复杂度之间的折衔。在这种工作中，我们比较了多种统计模型选择标准，例如 bayesian信息 критерион。使用SAS数据 collected by an autonomous underwater vehicle在挪威Bergen coast rocky environment中，使用HISAS-1032 synthetic aperture sonar系统的数据作为示例。
Protecting the Future of Information: LOCO Coding With Error Detection for DNA Data Storage
paper_authors: Canberk İrimağzı, Yusuf Uslan, Ahmed Hareedy
for: 本文研究了使用新引入的 lexicographically-ordered constrained（LOCO）码在 DNA 数据存储中。
methods: 本文提出了基于 ${A,T,G,C}$ 字母的 DNA LOCO（D-LOCO）码，并提供了编码-解码规则。这些规则提供了可靠的编码-解码算法，并且可以轻松地重新配置。
results: 本文的编码-解码算法比现有Literature中的算法更有效，并且可以实现高率的 DNA 数据存储。此外，本文还提出了四种方案来连接 consecutive codewords，其中三种方案可以确定单个替换错误检测每个 codeword。Abstract
DNA strands serve as a storage medium for $4$-ary data over the alphabet $\{A,T,G,C\}$. DNA data storage promises formidable information density, long-term durability, and ease of replicability. However, information in this intriguing storage technology might be corrupted. Experiments have revealed that DNA sequences with long homopolymers and/or with low $GC$-content are notably more subject to errors upon storage. This paper investigates the utilization of the recently-introduced method for designing lexicographically-ordered constrained (LOCO) codes in DNA data storage. This paper introduces DNA LOCO (D-LOCO) codes, over the alphabet $\{A,T,G,C\}$ with limited runs of identical symbols. These codes come with an encoding-decoding rule we derive, which provides affordable encoding-decoding algorithms. In terms of storage overhead, the proposed encoding-decoding algorithms outperform those in the existing literature. Our algorithms are readily reconfigurable. D-LOCO codes are intrinsically balanced, which allows us to achieve balancing over the entire DNA strand with minimal rate penalty. Moreover, we propose four schemes to bridge consecutive codewords, three of which guarantee single substitution error detection per codeword. We examine the probability of undetecting errors. We also show that D-LOCO codes are capacity-achieving and that they offer remarkably high rates at moderate lengths.
摘要
This paper investigates the utilization of the recently-introduced method for designing lexicographically-ordered constrained (LOCO) codes in DNA data storage. This paper introduces DNA LOCO (D-LOCO) codes, over the alphabet $\{\mathtt{A}, \mathtt{T}, \mathtt{G}, \mathtt{C}\}$ with limited runs of identical symbols. These codes come with an encoding-decoding rule we derive, which provides affordable encoding-decoding algorithms. In terms of storage overhead, the proposed encoding-decoding algorithms outperform those in the existing literature. Our algorithms are readily reconfigurable. D-LOCO codes are intrinsically balanced, which allows us to achieve balancing over the entire DNA strand with minimal rate penalty. Moreover, we propose four schemes to bridge consecutive codewords, three of which guarantee single substitution error detection per codeword. We examine the probability of undetecting errors. We also show that D-LOCO codes are capacity-achieving and that they offer remarkably high rates at moderate lengths.
Resource Efficient Over-the-Air Fronthaul Signaling for Uplink Cell-Free Massive MIMO Systems
paper_authors: Zakir Hussain Shaik, Sai Subramanyam Thoota, Emil Björnson, Erik G. Larsson
for: addresses the demanding requirements of uplink (UL) fronthaul in cell-free massive multiple-input multiple-output (MIMO) systems.
methods: proposes a novel resource efficient analog over-the-air (OTA) computation framework, including transmit precoding and two-phase power assignment strategies at the access points (APs).
results: derives analytical expressions for the Bayesian and classical estimators of the OTA combined signals, and empirically evaluates the normalized mean square error (NMSE), symbol error rate (SER), and coded bit error rate (BER) of the developed solution, showing that it outperforms the state-of-the-art wired fronthaul based system.Abstract
We propose a novel resource efficient analog over-the-air (OTA) computation framework to address the demanding requirements of the uplink (UL) fronthaul between the access points (APs) and the central processing unit (CPU) in cell-free massive multiple-input multiple-output (MIMO) systems. We discuss the drawbacks of the wired and wireless fronthaul solutions, and show that our proposed mechanism is efficient and scalable as the number of APs increases. We present the transmit precoding and two-phase power assignment strategies at the APs to coherently combine the signals OTA in a spectrally efficient manner. We derive the statistics of the APs locally available signals which enable us to to obtain the analytical expressions for the Bayesian and classical estimators of the OTA combined signals. We empirically evaluate the normalized mean square error (NMSE), symbol error rate (SER), and the coded bit error rate (BER) of our developed solution and benchmark against the state-of-the-art wired fronthaul based system
摘要
我们提出了一种新的资源有效的无线上空计算框架，以满足Cell-free巨量多输入多输出系统的上行（UL）前方向的需求。我们讨论了有线和无线前方向解决方案的缺点，并显示了我们的提议机制具有规模可扩展的优点。我们介绍了在AP上进行预编码和两相电压分配策略，以具有spectral efficiency的方式将OTA信号相乘。我们 derivation了AP上可用信号的统计，允许我们获得OTA相乘后的分布统计。我们Empirically评估了NMSE、SER和BER表现，并与现有的有线前方向基础系统进行比较。Note: Please note that 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.
Maximum Eigenvalue Detection based Spectrum Sensing in RIS-aided System with Correlated Fading
results: 我们 derivated了测试统计量的正态分布 under null和signal present假设下。然后，我们使用这些结果计算了假设检测的false alarm和 detection probabilities。此外，我们还优化了RIS的相位旋转矩阵，以提高检测性能。我们的numerical analysis表明，MED的接收操作特征曲线在RIS元素增加、SNR提高和 statistically optimal配置RIS的情况下都得到提高。Abstract
Robust spectrum sensing is crucial for facilitating opportunistic spectrum utilization for secondary users (SU) in the absense of primary users (PU). However, propagation environment factors such as multi-path fading, shadowing, and lack of line of sight (LoS) often adversely affect detection performance. To deal with these issues, this paper focuses on utilizing reconfigurable intelligent surfaces (RIS) to improve spectrum sensing in the scenario wherein both the multi-path fading and noise are correlated. In particular, to leverage the spatially correlated fading, we propose to use maximum eigenvalue detection (MED) for spectrum sensing. We first derive exact distributions of test statistics, i.e., the largest eigenvalue of the sample covariance matrix, observed under the null and signal present hypothesis. Next, utilizing these results, we present the exact closed-form expressions for the false alarm and detection probabilities. In addition, we also optimally configure the phase shift matrix of RIS such that the mean of the test statistics is maximized, thus improving the detection performance. Our numerical analysis demonstrates that the MED's receiving operating characteristic (ROC) curve improves with increased RIS elements, SNR, and the utilization of statistically optimal configured RIS.
摘要
Robust spectrum sensing 是次级用户（SU）在主要用户（PU）缺 absent 的情况下促进机会性spectrum utilization的关键。然而，传播环境因素 such as 多 PATH 抑制、阴影和无线线 sight（LoS） frequently adversely affect detection performance. To address these issues, this paper focuses on using reconfigurable intelligent surfaces (RIS) to improve spectrum sensing in the scenario where both multi-path fading and noise are correlated. In particular, we propose to use maximum eigenvalue detection (MED) for spectrum sensing. We first derive the exact distributions of test statistics, i.e., the largest eigenvalue of the sample covariance matrix, observed under the null and signal present hypotheses. Next, we use these results to present the exact closed-form expressions for the false alarm and detection probabilities. Additionally, we optimize the phase shift matrix of RIS such that the mean of the test statistics is maximized, thus improving detection performance. Our numerical analysis shows that the MED's receiving operating characteristic (ROC) curve improves with increased RIS elements, SNR, and the utilization of statistically optimal configured RIS.Note that the translation is in Simplified Chinese, which is one of the two standard forms of Chinese writing. The other form is Traditional Chinese.
Joint Location Sensing and Channel Estimation for IRS-Aided mmWave ISAC Systems
paper_authors: Zijian Chen, Ming-Min Zhao, Min Li, Fan Xu, Qingqing Wu, Min-Jian Zhao
for: 本研究 investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system, aiming to jointly sense the target/scatterer/user positions and estimate the sensing and communication (SAC) channels.
methods: 提议 a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase using scanning-based IRS reflection coefficients and the second phase using optimized IRS reflection coefficients. The proposed algorithm combines VBI, messaging passing, and expectation-maximization (EM) methods to solve the considered joint location sensing and CE problem, exploiting the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels.
results: simulation results show the superiority of the proposed transmission scheme and associated algorithms, verifying the effectiveness of the self-sensing IRS in reducing the path loss of sensing-related links and enhancing the overall performance of the ISAC system.Abstract
In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly sense the target/scatterer/user positions as well as estimate the sensing and communication (SAC) channels in the considered system, we propose a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase (using scanning-based IRS reflection coefficients) and second phase (using optimized IRS reflection coefficients). For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cram\'{e}r-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms.
摘要
在这篇论文中，我们研究了一种自适应智能反射表面（IRS）帮助毫米波（mmWave）集成感知通信（ISAC）系统。与传统的仅PASSIVE IRS不同，自适应IRS可以有效减少感知相关链路的覆盖距离，从而在ISAC系统中具有优势。为了同时感知目标/散射体/用户位置以及估计感知通信（SAC） канала，我们提议了两个阶段的传输方案，其中第一阶段使用扫描基于IRS反射率来获得粗略的感知频道估计结果，第二阶段使用优化IRS反射率来获得精度的感知频道估计结果。 For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cramér-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms.
paper_authors: Yang Lu, Ming-Min Zhao, Ming Lei, Min-Jian Zhao
for: 这个论文的目的是提高短至中等长度楔码的快速解码性能，尤其是在低延迟通信场景中。
methods: 本论文使用了特定楔码子代码（special nodes）的快速列解算法，包括单元检查（SPC）节点和一个或多个单元检查（SR1/SPC）节点。具体来说，本论文提出了两种快速列解算法，其中第一种使用了预序解码程式，使解码时间linear with the list size，第二种则透过在线上预决缺失路径来进一步平行化解码过程，实现更快的解码速度。
results: simulations results show that the proposed list decoding algorithms are able to achieve up to 70.7% lower decoding latency than state-of-the-art fast SCL decoders, while exhibiting the same error-correction performance.Abstract
Due to the ability to provide superior error-correction performance, the successive cancellation list (SCL) algorithm is widely regarded as one of the most promising decoding algorithms for polar codes with short-to-moderate code lengths. However, the application of SCL decoding in low-latency communication scenarios is limited due to its sequential nature. To reduce the decoding latency, developing tailored fast and efficient list decoding algorithms of specific polar substituent codes (special nodes) is a promising solution. Recently, fast list decoding algorithms are proposed by considering special nodes with low code rates. Aiming to further speedup the SCL decoding, this paper presents fast list decoding algorithms for two types of high-rate special nodes, namely single-parity-check (SPC) nodes and sequence rate one or single-parity-check (SR1/SPC) nodes. In particular, we develop two classes of fast list decoding algorithms for these nodes, where the first class uses a sequential decoding procedure to yield decoding latency that is linear with the list size, and the second further parallelizes the decoding process by pre-determining the redundant candidate paths offline. Simulation results show that the proposed list decoding algorithms are able to achieve up to 70.7\% lower decoding latency than state-of-the-art fast SCL decoders, while exhibiting the same error-correction performance.
摘要
由于可提供出色的错误纠正性表现，连续取消列表（SCL）算法在短至中型编码长度的楔码中广泛被视为一种最有前途的解码算法。然而，在低延迟通信场景中，SCL解码的应用受到其顺序性的限制。为了降低解码延迟，开发专门为特定楔substituent代码（特定节点）设计快速高效的列解算法是一个有前途的解决方案。在最近，为了提高SCL解码的速度，这篇论文提出了两种类型的高速列解算法，即单元性检查（SPC）节点和序列率一（SR1/SPC）节点。具体来说，我们开发了两类快速列解算法，其中第一类使用顺序解码过程，使解码延迟线性增长与列表大小相关；第二类进一步平行化解码过程，通过先行确定冗余候选道路来提高速度。实验结果表明，提议的列解算法可以与当前最速的SCL解码器相比，实现70.7%的解码延迟降低，同时保持同等的错误纠正性表现。
Channel Estimation with Dynamic Metasurface Antennas via Model-Based Learning
paper_authors: Xiangyu Zhang, Haiyang Zhang, Luxi Yang, Yonina C. Eldar for:This paper proposes two model-based learning methods to overcome the challenge of channel estimation in multiple input multiple output (MIMO) communication systems using dynamic metasurface antennas (DMAs).methods:The proposed methods use a combination of random DMA weighting matrices and spatial gridding dictionaries to form the sensing matrix, and employ the learned iterative shrinkage and thresholding algorithm (LISTA) to recover the sparse channel parameters. Additionally, a self-supervised learning technique is proposed to tackle the difficulty of acquiring noise-free data.results:The proposed methods demonstrate better channel accuracy than traditional sparse recovery methods and the sensing matrix optimization technique achieves better channel accuracy than the baseline method.Abstract
Dynamic Metasurface Antenna (DMA) is a cutting-edge antenna technology offering scalable and sustainable solutions for large antenna arrays. The effectiveness of DMAs stems from their inherent configurable analog signal processing capabilities, which facilitate cost-limited implementations. However, when DMAs are used in multiple input multiple output (MIMO) communication systems, they pose challenges in channel estimation due to their analog compression. In this paper, we propose two model-based learning methods to overcome this challenge. Our approach starts by casting channel estimation as a compressed sensing problem. Here, the sensing matrix is formed using a random DMA weighting matrix combined with a spatial gridding dictionary. We then employ the learned iterative shrinkage and thresholding algorithm (LISTA) to recover the sparse channel parameters. LISTA unfolds the iterative shrinkage and thresholding algorithm into a neural network and trains the neural network into a highly efficient channel estimator fitting with the previous channel. As the sensing matrix is crucial to the accuracy of LISTA recovery, we introduce another data-aided method, LISTA-sensing matrix optimization (LISTA-SMO), to jointly optimize the sensing matrix. LISTA-SMO takes LISTA as a backbone and embeds the sensing matrix optimization layers in LISTA's neural network, allowing for the optimization of the sensing matrix along with the training of LISTA. Furthermore, we propose a self-supervised learning technique to tackle the difficulty of acquiring noise-free data. Our numerical results demonstrate that LISTA outperforms traditional sparse recovery methods regarding channel estimation accuracy and efficiency. Besides, LISTA-SMO achieves better channel accuracy than LISTA, demonstrating the effectiveness in optimizing the sensing matrix.
摘要
dynamically metasurface antenna (DMA) 是一种前沿的天线技术，具有可扩展和可持续的解决方案。 DMA 的可 configurable 分析信号处理能力使其在成本限制下实现高效性。 然而，在多输入多输出 (MIMO) 通信系统中使用 DMA 会带来频道估计的挑战，因为 DMA 的分析压缩会导致频道估计困难。在这篇论文中，我们提出了两种基于模型学习方法来解决这个挑战。我们的方法是将频道估计视为压缩感知问题，其中感知矩阵由随机 DMA 质量矩阵和空间格点词典组成。然后，我们使用学习舒缩和阈值算法 (LISTA) 来恢复稀疏频道参数。LISTA 将舒缩和阈值算法拓展成神经网络，并在神经网络中培训一个高效的频道估计器，与之前的频道相符。在感知矩阵对于 LISTA 的准确性很重要，我们因此引入了另一种数据帮助的方法，即 LISTA-感知矩阵优化 (LISTA-SMO)。LISTA-SMO 将 LISTA 作为后备，并将感知矩阵优化层 embedding 在 LISTA 神经网络中，以同时优化感知矩阵和 LISTA 的训练。此外，我们还提出了一种无supervision learning技术，以解决获取干净数据的困难。我们的数字结果表明，LISTA 比传统稀疏恢复方法更高效和准确地进行频道估计。此外，LISTA-SMO 在频道准确性方面比 LISTA 高，证明了感知矩阵优化的效果。
Joint Source-Channel Coding for Channel-Adaptive Digital Semantic Communications
results: 这个方法在实验中被证明可以对数位图像分类和重建任务进行改进，比较现有的 JSCC 方法表现更好。Abstract
In this paper, we propose a novel joint source-channel coding (JSCC) approach for channel-adaptive digital semantic communications. In semantic communication systems with digital modulation and demodulation, end-to-end training and robust design of JSCC encoder and decoder becomes challenging due to the nonlinearity of modulation and demodulation processes, as well as diverse channel conditions and modulation orders. To address this challenge, we first develop a new demodulation method which assesses the uncertainty of the demodulation output to improve the robustness of the digital semantic communication system. We then devise a robust training strategy that facilitates end-to-end training of the JSCC encoder and decoder, while enhancing their robustness and flexibility. To this end, we model the relationship between the encoder's output and decoder's input using binary symmetric erasure channels and then sample the parameters of these channels from diverse distributions. We also develop a channel-adaptive modulation technique for an inference phase, in order to reduce the communication latency while maintaining task performance. In this technique, we adaptively determine modulation orders for the latent variables based on channel conditions. Using simulations, we demonstrate the superior performance of the proposed JSCC approach for both image classification and reconstruction tasks compared to existing JSCC approaches.
摘要
在这篇论文中，我们提出了一种新的联合源码混合（JSCC）方法，用于适应通道condition下的数字semantic通信系统。在数字模ulation和демодуляción中，因为模ulation和демодуляción过程的非线性，以及不同通道条件和模ulationOrder，所以JSCC编码器和解码器的端到端训练和稳定设计变得挑战性更高。为 Addressing this challenge, we first develop a new demodulation method that assesses the uncertainty of the demodulation output to improve the robustness of the digital semantic communication system. We then devise a robust training strategy that facilitates end-to-end training of the JSCC encoder and decoder, while enhancing their robustness and flexibility. To this end, we model the relationship between the encoder's output and decoder's input using binary symmetric erasure channels, and then sample the parameters of these channels from diverse distributions. We also develop a channel-adaptive modulation technique for an inference phase, in order to reduce the communication latency while maintaining task performance. In this technique, we adaptively determine modulation orders for the latent variables based on channel conditions. Using simulations, we demonstrate the superior performance of the proposed JSCC approach for both image classification and reconstruction tasks compared to existing JSCC approaches.
On the View-and-Channel Aggregation Gain in Integrated Sensing and Edge AI
paper_authors: Xu Chen, Khaled B. Letaief, Kaibin Huang
For: The paper is written to explore the fundamental performance gains of view-and-channel aggregation in Integrated sensing and edge AI (ISEA) systems for Internet-of-Things (IoT) applications.* Methods: The paper uses a well-established distribution model of multi-view sensing data, which is modified to represent individual sensor observation perspectives. The authors also use a novel approach involving a scaling-tight uncertainty surrogate function, global discriminant gain, distribution of receive Signal-to-Noise Ratio (SNR), and channel induced discriminant loss to study the End-to-End sensing (inference) uncertainty of the ISEA system.* Results: The paper shows that the End-to-End sensing uncertainty diminishes at an exponential rate as the number of views/sensors grows, with a rate proportional to global discriminant gain. Additionally, the authors find that the exponential scaling remains even with channel distortion, but with a reduced decay rate related to the channel induced discriminant loss. The insights from the paper are validated by experiments using real-world dataset.Abstract
Sensing and edge artificial intelligence (AI) are two key features of the sixth-generation (6G) mobile networks. Their natural integration, termed Integrated sensing and edge AI (ISEA), is envisioned to automate wide-ranging Internet-of-Tings (IoT) applications. To achieve a high sensing accuracy, multi-view features are uploaded to an edge server for aggregation and inference using an AI model. The view aggregation is realized efficiently using over-the-air computing (AirComp), which also aggregates channels to suppress channel noise. At its nascent stage, ISEA still lacks a characterization of the fundamental performance gains from view-and-channel aggregation, which motivates this work. Our framework leverages a well-established distribution model of multi-view sensing data where the classic Gaussian-mixture model is modified by adding sub-spaces matrices to represent individual sensor observation perspectives. Based on the model, we study the End-to-End sensing (inference) uncertainty, a popular measure of inference accuracy, of the said ISEA system by a novel approach involving designing a scaling-tight uncertainty surrogate function, global discriminant gain, distribution of receive Signal-to-Noise Ratio (SNR), and channel induced discriminant loss. We prove that the E2E sensing uncertainty diminishes at an exponential rate as the number of views/sensors grows, where the rate is proportional to global discriminant gain. Given channel distortion, we further show that the exponential scaling remains with a reduced decay rate related to the channel induced discriminant loss. Furthermore, we benchmark AirComp against equally fast, traditional analog orthogonal access, which reveals a sensing-accuracy crossing point between the schemes, leading to the proposal of adaptive access-mode switching. Last, the insights from our framework are validated by experiments using real-world dataset.
摘要
sixth-generation (6G) 无线网络中的感知和边缘人工智能（AI）是两个关键特点。将它们天然地融合起来，称为 интеegrated sensing and edge AI（ISEA），可以自动执行广泛的互联网东西（IoT）应用。以实现高精度感知，多视图特征被上传到边缘服务器进行聚合和推理使用AI模型。视图聚合可以高效地实现使用空中计算（AirComp），同时也可以聚合通道来抑制通道噪声。在它的早期阶段，ISEA仍然缺乏对视图和通道聚合的基本性能提升的Characterization，这种 motivates this work。我们的框架利用了已有的多视图感知数据分布模型，其中类型的 Gaussian-mixture model 被修改为包含个人感知观察角度的子空间矩阵。基于模型，我们研究ISEA系统的端到端感知（推理）uncertainty，一种流行的推理准确度度量，通过一种新的扩展紧急函数、全球Discriminant gain、接收Signal-to-Noise Ratio（SNR）分布和通道引起的Discriminant loss来研究。我们证明，端到端感知uncertainty在视图/感知器数量增加时 exponentially decay，其速率与全球Discriminant gain相关。在存在通道扭曲时，我们进一步证明，扩展 decay rate 与通道引起的Discriminant loss相关。此外，我们对AirComp和传统的Analog orthogonal access进行比较，发现感知准确度 crossing point между两种方案，导致了对接入模式的自适应 switching。最后，我们的框架的发现被实际 dataset 验证。
Learning Bayes-Optimal Channel Estimation for Holographic MIMO in Unknown EM Environments
paper_authors: Wentao Yu, Hengtao He, Xianghao Yu, Shenghui Song, Jun Zhang, Ross D. Murch, Khaled B. Letaief
For: The paper is written for future 6G systems that use holographic MIMO (HMIMO) technology, which requires efficient channel estimation in arbitrary and unknown EM environments.* Methods: The paper proposes a self-supervised minimum mean-square-error (MMSE) channel estimation algorithm based on powerful machine learning tools, including score matching and principal component analysis. The training stage requires only the pilot signals, without needing to know the spatial correlation, the ground-truth channels, or the received signal-to-noise-ratio.* Results: The proposed algorithm can approach the performance of the oracle MMSE method with an extremely low complexity, making it a competitive candidate in practice.Here is the same information in Simplified Chinese:* For: 这篇论文是为未来的6G系统而写的，该系统使用束幂MIMO（HMIMO）技术，需要高效的通道估计在不知道的EM环境中。* Methods: 论文提出一种基于强大机器学习工具的自助学习最小均方差（MMSE）通道估计算法，包括分数匹配和主成分分析。训练阶段只需要各个批处理信号，不需要知道空间相关性、真实通道分布或接收信号噪声级。* Results: 提议的算法可以接近oracle MMSE方法的性能，但具有极低的复杂性，使其在实践中成为竞争力强的候选人。Abstract
Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel. Nevertheless, the promised gain of HMIMO could not be fully unleashed without an efficient means to estimate the high-dimensional channel. Bayes-optimal estimators typically necessitate either a large volume of supervised training samples or a priori knowledge of the true channel distribution, which could hardly be available in practice due to the enormous system scale and the complicated EM environments. It is thus important to design a Bayes-optimal estimator for the HMIMO channels in arbitrary and unknown EM environments, free of any supervision or priors. This work proposes a self-supervised minimum mean-square-error (MMSE) channel estimation algorithm based on powerful machine learning tools, i.e., score matching and principal component analysis. The training stage requires only the pilot signals, without knowing the spatial correlation, the ground-truth channels, or the received signal-to-noise-ratio. Simulation results will show that, even being totally self-supervised, the proposed algorithm can still approach the performance of the oracle MMSE method with an extremely low complexity, making it a competitive candidate in practice.
摘要
依 Moore 多规模 MIMO (HMIMO) 在未来的 6G 系统中被认为是一个有前途的推动者，通过在受限空间内部署ULTRA 大量天线来利用电磁频谱（EM）频道的传播特性。然而， promise 的 HMIMO 潜在优势尚未得到充分发挥，因为需要一种有效的高维度通道估计方法。 Bayes 优化的估计器通常需要大量的supervised 训练样本或者假设true 通道分布，这些样本或分布在实际中很难获得，因为系统规模很大，EM 环境复杂。因此，这种工作提议了一种基于强大机器学习工具的自主Supervised 最小二乘误差（MMSE）通道估计算法。该算法只需要启动阶段的导航信号，不需要知道空间相关性，真实通道，或接收信号噪听率。 simulation 结果表明，即使完全自主，提议的算法仍然可以接近 oracle MMSE 方法的性能，而且具有极低的复杂度，使其在实践中成为竞争力强的候选人。
Passive Human Sensing Enhanced by Reconfigurable Intelligent Surface: Opportunities and Challenges
results: 论文提出了基于RIS的微动诊断系统，并通过实验证明了这种技术在检测生理指标的潜在潜力。 finally, 论文还讨论了这一领域的技术挑战和机遇。Abstract
Reconfigurable intelligent surfaces (RISs) have flexible and exceptional performance in manipulating electromagnetic waves and customizing wireless channels. These capabilities enable them to provide a plethora of valuable activity-related information for promoting wireless human sensing. In this article, we present a comprehensive review of passive human sensing using radio frequency signals with the assistance of RISs. Specifically, we first introduce fundamental principles and physical platform of RISs. Subsequently, based on the specific applications, we categorize the state-of-the-art human sensing techniques into three types, including human imaging,localization, and activity recognition. Meanwhile, we would also investigate the benefits that RISs bring to these applications. Furthermore, we explore the application of RISs in human micro-motion sensing, and propose a vital signs monitoring system enhanced by RISs. Experimental results are presented to demonstrate the promising potential of RISs in sensing vital signs for manipulating individuals. Finally, we discuss the technical challenges and opportunities in this field.
摘要
可重配置智能表面（RIS）具有 flexible 和异常表现，可以控制电磁波和自定义无线通道。这些能力使其能提供许多有价值的活动相关信息，以便促进无线人员感知。在这篇文章中，我们提供了无线人员感知的全面回顾，特别是通过 RIS 的帮助实现的。我们首先介绍 RIS 的基本原理和物理平台。然后，根据应用场景，我们将现有的人类感知技术分为三类，包括人像、本地化和活动识别。此外，我们还 investigate RIS 在人微动感知方面的应用，并提出了基于 RIS 的生命体矢量监测系统。实验结果表明，RIS 在感知生命体矢量方面具有普遍的潜力。最后，我们讨论了这一领域的技术挑战和机遇。
Cost-Efficient Computation Offloading and Service Chain Caching in LEO Satellite Networks
results: 研究结果显示，该方法可以降低总成本（包括任务延迟和能源消耗）约20%，相比于传统的资料中心架构。Abstract
The ever-increasing demand for ubiquitous, continuous, and high-quality services poses a great challenge to the traditional terrestrial network. To mitigate this problem, the mobile-edge-computing-enhanced low earth orbit (LEO) satellite network, which provides both communication connectivity and on-board processing services, has emerged as an effective method. The main issue in LEO satellites includes finding the optimal locations to host network functions (NFs) and then making offloading decisions. In this article, we jointly consider the problem of service chain caching and computation offloading to minimize the overall cost, which consists of task latency and energy consumption. In particular, the collaboration among satellites, the network resource limitations, and the specific operation order of NFs in service chains are taken into account. Then, the problem is formulated and linearized as an integer linear programming model. Moreover, to accelerate the solution, we provide a greedy algorithm with cubic time complexity. Numerical investigations demonstrate the effectiveness of the proposed scheme, which can reduce the overall cost by around 20% compared to the nominal case where NFs are served in data centers.
摘要
Translated into Simplified Chinese:随着服务需求的不断增长，传统的陆地网络面临着严重的挑战。为解决这问题，低轨道卫星网络（LEO），带有通信连接和机能处理服务，已经出现为有效的解决方案。LEO卫星中的主要问题是找到最佳的NF主机位置，然后做卸载决策。在这篇文章中，我们同时考虑服务链缓存和计算卸载问题，以最小化总成本，包括任务延迟和能耗总量。具体来说，我们考虑卫星之间的协作、网络资源的限制，以及服务链中NF的具体执行顺序。然后，我们将问题形式化并Linear化为整数线性 програм序列。此外，为加速解决，我们提供了一种 cubic 时间复杂度的急速算法。数值调查表明，我们的方案可以相比nominal情况下，降低总成本约20%。
On the IRS Deployment in Smart Factories Considering Blockage Effects: Collocated or Distributed?
results: 研究发现，对于高堵塞密度，分布部署可以提高预期接收噪声比率和预期FB容量；对于低堵塞密度，URLLC服务可以从分布部署中受益，而eMBB服务则不受分布部署的影响。Abstract
In this article, we study the collocated and distributed deployment of intelligent reflecting surfaces (IRS) for a fixed total number of IRS elements to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services inside a factory. We build a channel model that incorporates the line-of-sight (LOS) probability and power loss of each transmission path, and propose three metrics, namely, the expected received signal-to-noise ratio (SNR), expected finite-blocklength (FB) capacity, and expected outage probability, where the expectation is taken over the probability distributions of interior blockages and channel fading. The expected received SNR and expected FB capacity for extremely high blockage densities are derived in closed-form as functions of the amount and height of IRSs and the density, size, and penetration loss of blockages, which are verified by Monte Carlo simulations. Results show that deploying IRSs vertically higher leads to higher expected received SNR and expected FB capacity. By analysing the average/minimum/maximum of the three metrics versus the number of IRSs, we find that for high blockage densities, both eMBB and URLLC services benefit from distributed deployment; and for low blockage densities, URLLC services benefit from distributed deployment while eMBB services see limited difference between collocated and distributed deployment.
摘要
在这篇文章中，我们研究了彩色彩镜（IRS）的分布式部署，以支持内部的增强移动广播（eMBB）和低延迟低可靠通信（URLLC）服务。我们构建了一个通道模型，该模型包括每个传输路径的直视程度（LOS）概率和功率损失，并提出了三个指标，即预期的干扰比率（SNR）、预期的固定块长度（FB）容量，以及预期的失业概率。这三个指标的预期值在高封闭率下是几何函数，它们随着彩镜数量和封闭物体的密度、大小和渗透损失而变化。我们通过 Monte Carlo 仿真来验证这些结果。结果显示，彩镜高度越高，预期的干扰比率和固定块长度容量都越高。通过分析平均/最小/最大的三个指标对彩镜数量的影响，我们发现在高封闭率下， beiden eMBB 和 URLLC 服务都受益于分布式部署；在低封闭率下，URLLC 服务受益于分布式部署，而 eMBB 服务则不受分布式部署的影响。