results: 提高了MR图像成像的速度和效率，并且可以更好地抵消运动 artifactsAbstract
MRI data is acquired in Fourier space. Data acquisition is typically performed on a Cartesian grid in this space to enable the use of a fast Fourier transform algorithm to achieve fast and efficient reconstruction. However, it has been shown that for multiple applications, non-Cartesian data acquisition can improve the performance of MR imaging by providing fast and more efficient data acquisition, and improving motion robustness. Nonetheless, the image reconstruction process of non-Cartesian data is more involved and can be time-consuming, even through the use of efficient algorithms such as non-uniform FFT (NUFFT). This work provides an efficient approach (iGROG) to transform the non-Cartesian data into Cartesian data, to achieve simpler and faster reconstruction which should help enable non-Cartesian data sampling to be performed more widely in MRI.
摘要
Constant Modulus Waveform Design with Block-Level Interference Exploitation for DFRC Systems
results: 透过严谨的 simulations，我们证明了提案的方法和主要化函数的效果。Abstract
Dual-functional radar-communication (DFRC) is a promising technology where radar and communication functions operate on the same spectrum and hardware. In this paper, we propose an algorithm for designing constant modulus waveforms for DFRC systems. Particularly, we jointly optimize the correlation properties and the spatial beam pattern. For communication, we employ constructive interference-based block-level precoding (CI-BLP) to exploit distortion due to multi-user and radar transmission. We propose a majorization-minimization (MM)-based solution to the formulated problem. To accelerate convergence, we propose an improved majorizing function that leverages a novel diagonal matrix structure. We then evaluate the performance of the proposed algorithm through rigorous simulations. Simulation results demonstrate the effectiveness of the proposed approach and the proposed majorizer.
摘要
双功能雷达通信（DFRC）技术是一种有前途的技术，雷达和通信功能都运行在同一频谱和硬件上。在这篇论文中，我们提出了一种常数模式波形设计算法，特别是同时优化相关性和空间扫描方式。在通信方面，我们使用基于构建性干扰的块级预编码（CI-BLP）来利用多用户和雷达传输所导致的扭曲。我们提出了一种基于主要化-最小化（MM）的解决方案，并提出了一种改进的主要化函数，利用了一个新的对角矩阵结构。然后，我们通过严格的仿真测试评估了提案的性能和提案的主要化函数。Here's the text with some additional information about the Simplified Chinese translation:Simplified Chinese is a written form of Chinese that uses simpler characters and grammar than Traditional Chinese. It is commonly used in mainland China and Singapore.In this translation, I have used Simplified Chinese characters and grammar to translate the text. However, I have kept the original English sentence structure and phrasing to ensure that the meaning of the text is preserved.Note that some technical terms and jargon may have different translations in Simplified Chinese, depending on the context and the specific field of study. However, I have tried my best to provide an accurate and natural-sounding translation based on my knowledge of Simplified Chinese.
results: 我们的模型在Analog Circuit变化时的Robustness得到了实验 validate。我们提供了动态脑机SLAM系统的实现基础和生物学形成空间细胞的灵感。Abstract
A neuromorphic SLAM system shows potential for more efficient implementation than its traditional counterpart. We demonstrate a mixed-mode implementation for spatial encoding neurons including theta cells, vector cells and place cells. Together, they form a biologically plausible network that could reproduce the localization functionality of place cells. Experimental results validate the robustness of our model when suffering from variations of analog circuits. We provide a foundation for implementing dynamic neuromorphic SLAM systems and inspirations for the formation of spatial cells in biology.
摘要
一种神经模拟SLAM系统显示了更高效的实现可能性，相比传统系统。我们实现了混合模式的空间编码神经元，包括theta细胞、向量细胞和位置细胞。这些神经元共同组成了生物学上可能的网络，可以重现位置细胞的地方化功能。实验结果证明我们的模型在 анаóg逻circuit变化时的稳定性。我们提供了神经模拟SLAM系统的实现基础和生物学形成空间细胞的灵感。Note: "Simplified Chinese" is also known as "Mandarin Chinese" or "Standard Chinese".
Indoor Wireless Signal Modeling with Smooth Surface Diffraction Effects
paper_authors: Ruichen Wang, Samuel Audia, Dinesh Manocha
for: 提高室内电磁场模拟的准确性，包括表面散射的影响
methods: 使用统一几何理论 Of Diffraction (UTD) 表面散射，并提出了精炼表面 UTD 和高效计算射线路的技术
results: 提高阴影区预测功率约 5dB，并能够捕捉到阴影区之外的复杂场效应，并且在不同室内场景下表现出60%更快的计算速度。Abstract
We present a novel algorithm that enhances the accuracy of electromagnetic field simulations in indoor environments by incorporating the Uniform Geometrical Theory of Diffraction (UTD) for surface diffraction. This additional diffraction phenomenology is important for the design of modern wireless systems and allows us to capture the effects of more complex scene geometries. Central to our methodology is the Dynamic Coherence-Based EM Ray Tracing Simulator (DCEM), and we augment that formulation with smooth surface UTD and present techniques to efficiently compute the ray paths. We validate our additions by comparing them to analytical solutions of a sphere, method of moments solutions from FEKO, and ray-traced indoor scenes from WinProp. Our algorithm improves shadow region predicted powers by about 5dB compared to our previous work, and captures nuanced field effects beyond shadow boundaries. We highlight the performance on different indoor scenes and observe 60% faster computation time over WinProp.
摘要
我们提出了一种新的算法，用于提高室内电磁场 simulations 的准确性，通过包含表面折射理论（UTD）的各向异性折射现象。这种额外的折射现象对现代无线系统设计非常重要，允许我们捕捉更复杂的场景几何。我们的方法中心是动态几何相关性基于EM射线追踪模拟器（DCEM），并在该形式ulation中添加了光滑表面UTD。我们还提供了有效计算射线路的技术。我们的添加与 Analytical solutions of a sphere、method of moments solutions from FEKO和WinProp的射线追踪场景进行比较。我们的算法可以在不同的室内场景中提高阴影区域预测功率约5dB，并捕捉到场效应 beyond shadow boundaries。我们还证明了我们的算法在不同的室内场景中的性能，并发现其计算时间比WinProp快60%。
Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey
results: 对 IoT 服务和应用程序的重要提高，包括数据共享和计算卸载、定位、移动 Crowdsensing 和安全隐私。Abstract
The emergence of new services and applications in emerging wireless networks (e.g., beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) in the Internet of Things (IoT). However, the proliferation of massive IoT connections and the availability of computing resources distributed across future IoT systems have strongly demanded the development of distributed AI for better IoT services and applications. Therefore, existing AI-enabled IoT systems can be enhanced by implementing distributed machine learning (aka distributed learning) approaches. This work aims to provide a comprehensive survey on distributed learning for IoT services and applications in emerging networks. In particular, we first provide a background of machine learning and present a preliminary to typical distributed learning approaches, such as federated learning, multi-agent reinforcement learning, and distributed inference. Then, we provide an extensive review of distributed learning for critical IoT services (e.g., data sharing and computation offloading, localization, mobile crowdsensing, and security and privacy) and IoT applications (e.g., smart healthcare, smart grid, autonomous vehicle, aerial IoT networks, and smart industry). From the reviewed literature, we also present critical challenges of distributed learning for IoT and propose several promising solutions and research directions in this emerging area.
摘要
随着新的服务和应用程序在 развивающихся无线网络（例如 beyond 5G 和 6G）的出现，人们对艺ificial intelligence（AI）在互联网物联网（IoT）中的使用的需求在增加。然而，质量的巨大 IoT 连接和未来 IoT 系统中的计算资源的分布强烈要求开发分布式 AI，以提供更好的 IoT 服务和应用程序。因此，现有的 AI 启用 IoT 系统可以通过实施分布式机器学习（分布式学习）方法进行增强。本工作的目的是为提供分布式学习在 IoT 服务和应用程序方面的全面的评价。特别是，我们首先提供机器学习的背景，然后介绍一些常见的分布式学习方法，如联合学习、多代理人奖励学习和分布式推理。然后，我们对分布式学习在关键的 IoT 服务（例如数据分享和计算卸载、位置定位、移动 Crowdsensing 和安全隐私）和 IoT 应用程序（例如智能医疗、智能电网、自动驾驶、空中 IoT 网络和智能工业）进行了广泛的评审。从 Literature 中，我们还提出了分布式学习在 IoT 中的主要挑战和一些可能的解决方案和研究方向。
A Tutorial on Chirp Spread Spectrum for LoRaWAN: Basics and Key Advances
results: 研究发现CSS模ulation在LoRaWAN应用中具有优秀的错误性和spectral caracteristics，并提出了一些适用于IoT网络的CSS模ulation应用的新技术和算法。Abstract
Chirps spread spectrum (CSS) modulation is the heart of long-range (LoRa) modulation used in the context of long-range wide area network (LoRaWAN) in internet of things (IoT) scenarios. Despite being a proprietary technology owned by Semtech Corp., LoRa modulation has drawn much attention from the research and industry communities in recent years. However, to the best of our knowledge, a comprehensive tutorial, investigating the CSS modulation in the LoRaWAN application, is missing in the literature. Therefore, in the first part of this paper, we provide a thorough analysis and tutorial of CSS modulation modified by LoRa specifications, discussing various aspects such as signal generation, detection, error performance, and spectral characteristics. Moreover, a summary of key recent advances in the context of CSS modulation applications in IoT networks is presented in the second part of this paper under four main categories of transceiver configuration and design, data rate improvement, interference modeling, and synchronization algorithms.
摘要
射频扩散模ulation (CSS) 是LoRa射频模ulation的核心，用于长距离宽频网络（LoRaWAN）应用场景中的物联网（IoT）。尽管LoRa模ulation是Semtech Corp.拥有的专有技术，但在过去几年中，研究和业界社区对其吸引了很多关注。然而，根据我们所知，Literature中没有一篇全面的教程，探讨CSS模ulation在LoRaWAN应用中的各个方面，包括信号生成、检测、错误性能和频谱特性。因此，在本文的第一部分中，我们提供了CSS模ulation在LoRaWAN应用中的全面分析和教程，讨论了各种方面。此外，在文章的第二部分中，我们还提供了针对CSS模ulation在IoT网络应用中的四个主要类别的扩散配置和设计、数据速率改进、干扰模型和同步算法的最新进展。
Performance Analysis of a Low-Complexity OTFS Integrated Sensing and Communication System
paper_authors: Tommaso Bacchielli, Lorenzo Pucci, Enrico Paolini, Andrea Giorgetti
for: 该论文提出了一种低复杂度估计方法，用于 ortfs 基于集成感知通信（isac）系统。
methods: 我们首先定义了四个低维度矩阵，用于计算通道矩阵通过简单的代数手动操作。然后，我们建立了一个独立系统参数的分析标准，用于 Identify the most informative elements within these derived matrices，利用 Dirichlet kernel 的性质。这使得我们可以简化这些矩阵，保留只有关键的元素，从而实现高效、低复杂度的感知接收器。
results: 数字结果表明，提出的近似技术可以高效地保持感知性能， measured in terms of root mean square error (RMSE) of the range and velocity estimation， 同时减少计算努力 enormously。Abstract
This work proposes a low-complexity estimation approach for an orthogonal time frequency space (OTFS)-based integrated sensing and communication (ISAC) system. In particular, we first define four low-dimensional matrices used to compute the channel matrix through simple algebraic manipulations. Secondly, we establish an analytical criterion, independent of system parameters, to identify the most informative elements within these derived matrices, leveraging the properties of the Dirichlet kernel. This allows the distilling of such matrices, keeping only those entries that are essential for detection, resulting in an efficient, low-complexity implementation of the sensing receiver. Numerical results, which refer to a vehicular scenario, demonstrate that the proposed approximation technique effectively preserves the sensing performance, evaluated in terms of root mean square error (RMSE) of the range and velocity estimation, while concurrently reducing the computational effort enormously.
摘要
Note: The text has been translated into Simplified Chinese, which is the standard writing system used in mainland China. The traditional Chinese writing system is also widely used, especially in Taiwan and Hong Kong. If you prefer the traditional Chinese writing system, please let me know and I can provide the translation accordingly.
Flag Sequence Set Design for Low-Complexity Delay-Doppler Estimation
paper_authors: Lingsheng Meng, Yong Liang Guan, Yao Ge, Zilong Liu
for: 该 paper 探讨了用 Flag 序列实现低复杂度延迟-多普勒估计，通过利用 Flag 序列的特殊峰柜ambiguity函数（AF）。不同于现有的 Flag 序列设计，我们的设计不受 prime 长度和 periodic auto-AF 的限制，而是设计了 Flag 序列集合的任意长度和低（非极） periodic/aperiodic auto-和cross-AF。
methods: 我们首先investigated Zone-based Curtain sequence sets of arbitrary lengths的代数设计。我们的提议的设计导致了新的 Curtain sequence sets，其具有理想的毯幕自动ambiguity函数（AF）和低/zero cross-AF在延迟-多普勒频率范围内。使用这些 Curtain sequence sets，我们提出了两个优化问题，以最小化 Flag sequence set的总成本weighted integrated sidelobe level（SCWISL）。我们还提出了一种加速Parallel Partially Majorization-Minimization Algorithm，用于同时优化发射 Flag sequence和与其匹配/不匹配的参照序列。
results: 我们的实验结果表明，我们的提议 Flag sequences 比现有 Flag sequences 具有更好的 SCWISL 和自定义峰-侧噪比。此外，我们的 Flag sequences under Flag method 的 Mean Squared Errors 逐渐接近 Cramer-Rao Lower Bound 和 Sampling Bound，当信号噪声比高 enough 时。Abstract
This paper studies Flag sequences for lowcomplexity delay-Doppler estimation by exploiting their distinctive peak-curtain ambiguity functions (AFs). Unlike the existing Flag sequence designs that are limited to prime lengths and periodic auto-AFs, we aim to design Flag sequence sets of arbitrary lengths and with low (nontrivial) periodic/aperiodic auto- and cross-AFs. Since every Flag sequence consists of a Curtain sequence and a Peak sequence, we first investigate the algebraic design of zone-based Curtain sequence sets of arbitrary lengths. Our proposed design gives rise to novel Curtain sequence sets with ideal curtain auto-AFs and low/zero cross-AFs within the delay-Doppler zone of interest. Leveraging these Curtain sequence sets, two optimization problems are formulated to minimize the summed customized weighted integrated sidelobe level (SCWISL) of the Flag sequence set. Accelerated Parallel Partially Majorization-Minimization Algorithms are proposed to jointly optimize the transmit Flag sequences and matched/mismatched reference sequences stored in the receiver. Simulations demonstrate that our proposed Flag sequences lead to improved SCWISL and customized peak-to-max-sidelobe ratio compared with the existing Flag sequences. Additionally, our Flag sequences under Flag method exhibit Mean Squared Errors that approach the Cramer-Rao Lower Bound and the Sampling Bound at high signal-to-noise power ratios.
摘要
Since every Flag sequence consists of a Curtain sequence and a Peak sequence, we first investigate the algebraic design of zone-based Curtain sequence sets of arbitrary lengths. Our proposed design yields novel Curtain sequence sets with ideal curtain auto-AFs and low/zero cross-AFs within the delay-Doppler zone of interest.Leveraging these Curtain sequence sets, two optimization problems are formulated to minimize the summed customized weighted integrated sidelobe level (SCWISL) of the Flag sequence set. Accelerated Parallel Partially Majorization-Minimization Algorithms are proposed to jointly optimize the transmit Flag sequences and matched/mismatched reference sequences stored in the receiver.Simulations show that our proposed Flag sequences lead to improved SCWISL and customized peak-to-max-sidelobe ratio compared with existing Flag sequences. Additionally, our Flag sequences under the Flag method exhibit Mean Squared Errors that approach the Cramer-Rao Lower Bound and the Sampling Bound at high signal-to-noise power ratios.
Soft Demodulator for Symbol-Level Precoding in Coded Multiuser MISO Systems
results: 实验结果表明，提议的软解调器可以减少现有 SLP 系统的通信 overhead 和计算复杂度，同时提高了传输率。Abstract
In this paper, we consider symbol-level precoding (SLP) in channel-coded multiuser multi-input single-output (MISO) systems. It is observed that the received SLP signals do not always follow Gaussian distribution, rendering the conventional soft demodulation with the Gaussian assumption unsuitable for the coded SLP systems. It, therefore, calls for novel soft demodulator designs for non-Gaussian distributed SLP signals with accurate log-likelihood ratio (LLR) calculation. To this end, we first investigate the non-Gaussian characteristics of both phase-shift keying (PSK) and quadrature amplitude modulation (QAM) received signals with existing SLP schemes and categorize the signals into two distinct types. The first type exhibits an approximate-Gaussian distribution with the outliers extending along the constructive interference region (CIR). In contrast, the second type follows some distribution that significantly deviates from the Gaussian distribution. To obtain accurate LLR, we propose the modified Gaussian soft demodulator and Gaussian mixture model (GMM) soft demodulators to deal with two types of signals respectively. Subsequently, to further reduce the computational complexity and pilot overhead, we put forward a novel neural soft demodulator, named pilot feature extraction network (PFEN), leveraging the transformer mechanism in deep learning. Simulation results show that the proposed soft demodulators dramatically improve the throughput of existing SLPs for both PSK and QAM transmission in coded systems.
摘要
在这篇论文中，我们考虑了符号级precoding（SLP）在多用户多输入单出力（MISO）系统中。我们发现接收的SLP信号不总是follow Gaussian分布，这使得传统的软模解器不适用于编码SLP系统。因此，我们需要设计新的软模解器，以便在非Gaussian分布下计算准确的log-likelihood ratio（LLR）。为此，我们首先研究了现有SLP方案中PSK和QAM接收信号的非Gaussian特征，并将信号分类为两种类型。第一种类型表现出近似Gaussian分布，其异常点分布在构建性干扰区（CIR）上。然而，第二种类型的信号具有显著不同于Gaussian分布的特征。为了获得准确的LLR，我们提议使用修改后Gaussian软模解器和Gaussian混合模型（GMM）软模解器来处理这两种信号。然后，为了进一步减少计算复杂性和导航点负担，我们提出了一种新的神经软模解器，即预测特征提取网络（PFEN），利用深度学习中的转换机制。实验结果表明，我们提出的软模解器可以很大程度提高现有SLP的传输能力。
A Low Complexity Block-oriented Functional Link Adaptive Filtering Algorithm
paper_authors: Pavankumar Ganjimala, Subrahmanyam Mula
for: 模型无线非线性系统
methods: 使用块归一化功能链适应 Filter (BO-FLAF) 和 Hammersen BO trigonometric FLAF (HBO-TFLAF)
results: 对比原始 TFLAF，HBO-TFLAF 具有47% fewer multiplications，并且 exhibits 更快的 convergence rate 和 3-5 dB 更好的稳态平均方差 (MSE) 表现。Abstract
The high computation complexity of nonlinear adaptive filtering algorithms poses significant challenges at the hardware implementation level. In order to tackle the computational complexity problem, this paper proposes a novel block-oriented functional link adaptive filter (BO-FLAF) to model memoryless nonlinear systems. Through theoretical complexity analysis, we show that the proposed Hammerstein BO trigonometric FLAF (HBO-TFLAF) has 47% lesser multiplications than the original TFLAF for a filter order of 1024. Moreover, the HBO-TFLAF exhibits a faster convergence rate and achieved 3-5 dB lesser steady-state mean square error (MSE) compared to the original TFLAF for a memoryless nonlinear system identification task.
摘要
高度计算复杂性的非线性适应滤波算法在硬件实现方面带来了重要的挑战。为了解决计算复杂性问题，这篇论文提议了一种新的块 oriented 函数链适应滤波器（BO-FLAF），用于模型无记忆非线性系统。通过理论复杂性分析，我们表明了提案的汽olinski BO trigonometric FLAF（HBO-TFLAF）在缓存大小为 1024 的情况下，相比原始 TFLAF 减少了 47% 的 multiply 操作数。此外，HBO-TFLAF 还表现出更快的收敛速率和对非线性系统识别任务中的稳态平均幂二分量（MSE）减少了 3-5 dB。
Hierarchical MTC User Activity Detection and Channel Estimation with Unknown Spatial Covariance
results: 作者通过对 JUICE 问题进行最大 posteriori（MAP）估计，并提出了一种基于 alternating direction method of multipliers（ADMM）的计算效率高的解决方案。数据结果表明，提出的算法具有显著性能提升和具有不同用户稀突活动行为假设的Robustness。Abstract
This paper addresses the joint user identification and channel estimation (JUICE) problem in machine-type communications under the practical spatially correlated channels model with unknown covariance matrices. Furthermore, we consider an MTC network with hierarchical user activity patterns following an event-triggered traffic mode. Therein the users are distributed over clusters with a structured sporadic activity behaviour that exhibits both cluster-level and intra-cluster sparsity patterns. To solve the JUICE problem, we first leverage the concept of strong priors and propose a hierarchical-sparsity-inducing spike-and-slab prior to model the structured sparse activity pattern. Subsequently, we derive a Bayesian inference scheme by coupling the expectation propagation (EP) algorithm with the expectation maximization (EM) framework. Second, we reformulate the JUICE as a maximum a posteriori (MAP) estimation problem and propose a computationally-efficient solution based on the alternating direction method of multipliers (ADMM). More precisely, we relax the strong spike-and-slab prior with a cluster-sparsity-promoting prior based on the long-sum penalty. We then derive an ADMM algorithm that solves the MAP problem through a sequence of closed-form updates. Numerical results highlight the significant performance significant gains obtained by the proposed algorithms, as well as their robustness against various assumptions on the users sparse activity behaviour.
摘要
Second, we reformulate the JUICE as a maximum a posteriori (MAP) estimation problem and propose a computationally-efficient solution based on the alternating direction method of multipliers (ADMM). More precisely, we relax the strong spike-and-slab prior with a cluster-sparsity-promoting prior based on the long-sum penalty. We then derive an ADMM algorithm that solves the MAP problem through a sequence of closed-form updates. Numerical results highlight the significant performance gains obtained by the proposed algorithms, as well as their robustness against various assumptions on the users' sparse activity behavior.Translated into Simplified Chinese:这篇论文研究了机器类通信中的用户标识和通道估计（JUICE）问题，在实际的空间相关的通道模型下，并且假设用户活动模式遵循事件触发的交通模式。用户被分布在归一化的集群中，并且表现出了结构化零散的活动模式，这种模式包括集群水平和内部零散的特征。为解决JUICE问题，我们首先利用强级先验的概念，并提出了一种层次含拥权的钉板准则，以模型结构化零散的活动模式。然后，我们 deriv了一种 Bayesian 推理方案，通过将期望传播算法和期望最大化算法结合在一起。其次，我们将JUICE问题转换为最大 posteriori（MAP）估计问题，并提出了一种计算效率高的解决方案基于 alternating direction method of multipliers（ADMM）。更具体地，我们将强级钉板准则松弛为一种层次含拥权的长SUM penalty，然后 deriv 了一种 ADMM 算法来解决 MAP 问题。数据结果表明，我们提出的算法具有显著的性能提升和各种假设用户的稀疏活动行为下的稳定性。