results: 研究在实验室中进行了densely occupied的实验，并达到了$\sim$0.3m的平均绝对准确性。此外，研究还表明，使用1Hz的范围测量可以获得相似的准确性。Abstract
Indoor wireless ranging localization is a promising approach for low-power and high-accuracy localization of wearable devices. A primary challenge in this domain stems from non-line of sight propagation of radio waves. This study tackles a fundamental issue in wireless ranging: the unpredictability of real-time multipath determination, especially in challenging conditions such as when there is no direct line of sight. We achieve this by fusing range measurements with inertial measurements obtained from a low cost Inertial Measurement Unit (IMU). For this purpose, we introduce a novel asymmetric noise model crafted specifically for non-Gaussian multipath disturbances. Additionally, we present a novel Levenberg-Marquardt (LM)-family trust-region adaptation of the iSAM2 fusion algorithm, which is optimized for robust performance for our ranging-IMU fusion problem. We evaluate our solution in a densely occupied real office environment. Our proposed solution can achieve temporally consistent localization with an average absolute accuracy of $\sim$0.3m in real-world settings. Furthermore, our results indicate that we can achieve comparable accuracy even with infrequent (1Hz) range measurements.
摘要
内部无线距离地标定是一种有前途的方法，用于低功耗高精度的设备的本地化。主要挑战在这个领域是无线波的非直线传播，尤其是在没有直接视线的情况下。本研究解决了无线距离中的一个基本问题，即实时多path决定的不可预测性，特别是在复杂的环境下。我们通过将距离测量与IMU获取的动量测量进行混合来实现这一点。为此，我们提出了一种特定于非泊松噪声的非泊松噪声模型。此外，我们还提出了一种基于LM家族的信任区改进版iSAM2融合算法，以便在我们的距离-IMU融合问题上实现Robust性能。我们在一个实际办公室环境中进行了评估。我们的提议的解决方案可以在实际情况下实现时间一致的地标定，准确性约为0.3米。此外，我们的结果表明，我们可以在1Hz的距离测量频率下实现相同的准确性。
Towards Robust and Efficient Communications for Urban Air Mobility
paper_authors: Dennis Becker, Lukas Schalk for: 本研究旨在开发一种特地适应未来城市空间的Drone-to-Drone通信和监测系统（DroneCAST），以确保安全运行。methods: 本研究使用了多链接方法，并在两架机器人上安装了实验Communication系统硬件原型。results: 实验结果表明，DroneCAST系统可以提供高效稳定的直接信息交换，并且可以应对高交通密度和紧急情况。Abstract
For the realization of the future urban air mobility, reliable information exchange based on robust and efficient communication between all airspace participants will be one of the key factors to ensure safe operations. Especially in dense urban scenarios, the direct and fast information exchange between drones based on Drone-to-Drone communications is a promising technology for enabling reliable collision avoidance systems. However, to mitigate collisions and to increase overall reliability, unmanned aircraft still lack a redundant, higher-level safety net to coordinate and monitor traffic, as is common in today's civil aviation. In addition, direct and fast information exchange based on ad hoc communication is needed to cope with the very short reaction times required to avoid collisions and to cope with the the high traffic densities. Therefore, we are developing a \ac{d2d} communication and surveillance system, called DroneCAST, which is specifically tailored to the requirements of a future urban airspace and will be part of a multi-link approach. In this work we discuss challenges and expected safety-critical applications that will have to rely on communications for \ac{uam} and present our communication concept and necessary steps towards DroneCAST. As a first step towards an implementation, we equipped two drones with hardware prototypes of the experimental communication system and performed several flights around the model city to evaluate the performance of the hardware and to demonstrate different applications that will rely on robust and efficient communications.
摘要
为实现未来的城市空中交通，可靠的信息交换基于强健和高效的通信 между所有空间参与者将是一个关键因素，以确保安全操作。尤其在紧张的城市场景下，直接和快速的机器人之间的通信是一种承诺的技术，用于实现可靠的碰撞避免系统。然而，为了减少碰撞和提高总可靠性，无人飞机仍然缺乏一种备用的、更高一级的安全网络，以协调和监测交通，如今的民航航空中拥有的。此外，直接和快速的信息交换基于随机通信可以适应非常短的应急响应时间和高通信密度。因此，我们正在开发一个D2D通信和监测系统，称为DroneCAST，该系统特地适应未来城市空间的需求，并将成为多链接方法的一部分。在这个工作中，我们讨论了未来城市空中交通中通信的挑战和预期的安全关键应用，并提出了我们的通信概念和实现DroneCAST所需的必要步骤。作为实现的第一步，我们将两架无人机设备了实验通信系统的硬件原型，并在模型城市附近进行了多次飞行，以评估硬件性能和示例不同应用，它们均需要强健和高效的通信。
Stein Variational Gradient Descent-based Detection For Random Access With Preambles In MTC
results: 比 Markov Chain Monte Carlo-based approaches 更高的检测精度。Abstract
Traditional preamble detection algorithms have low accuracy in the grant-based random access scheme in massive machine-type communication (mMTC). We present a novel preamble detection algorithm based on Stein variational gradient descent (SVGD) at the second step of the random access procedure. It efficiently leverages deterministic updates of particles for continuous inference. To further enhance the performance of the SVGD detector, especially in a dense user scenario, we propose a normalized SVGD detector with momentum. It utilizes the momentum and a bias correction term to reduce the preamble estimation errors during the gradient descent process. Simulation results show that the proposed algorithm performs better than Markov Chain Monte Carlo-based approaches in terms of detection accuracy.
摘要
传统的启语探测算法在大规模机器类通信（mMTC）中的授权随机访问方案中的准确率低。我们提出了基于Stein变量加速度下降（SVGD）的新启语探测算法，它在随机访问过程的第二步中高效地利用权值更新。为了进一步提高SVGD探测器的性能，特别是在密集用户场景下，我们提议一种归一化的SVGD探测器。它利用权值和偏移量来减少启语估计错误，并且使用滚动平均来降低启语探测的难度。实验结果表明，提出的算法在探测精度方面比Markov链 Монте卡洛-based方法表现更好。
Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approach
results: simulations 表明，提议的方案在低SNR范围和非高斯噪声下表现出较好的网络可靠性和robust性，并且与深度神经网络（DNN）的 Refer 方法相比，实现了3倍的辐射信息增加。Abstract
With the incredible results achieved from generative pre-trained transformers (GPT) and diffusion models, generative AI (GenAI) is envisioned to yield remarkable breakthroughs in various industrial and academic domains. In this paper, we utilize denoising diffusion probabilistic models (DDPM), as one of the state-of-the-art generative models, for probabilistic constellation shaping in wireless communications. While the geometry of constellations is predetermined by the networking standards, probabilistic constellation shaping can help enhance the information rate and communication performance by designing the probability of occurrence (generation) of constellation symbols. Unlike conventional methods that deal with an optimization problem over the discrete distribution of constellations, we take a radically different approach. Exploiting the ``denoise-and-generate'' characteristic of DDPMs, the key idea is to learn how to generate constellation symbols out of noise, ``mimicking'' the way the receiver performs symbol reconstruction. By doing so, we make the constellation symbols sent by the transmitter, and what is inferred (reconstructed) at the receiver become as similar as possible. Our simulations show that the proposed scheme outperforms deep neural network (DNN)-based benchmark and uniform shaping, while providing network resilience as well as robust out-of-distribution performance under low-SNR regimes and non-Gaussian noise. Notably, a threefold improvement in terms of mutual information is achieved compared to DNN-based approach for 64-QAM geometry.
摘要
“受到循环式训练的干扰抑制模型（DDPM）和扩散模型的惊人成果，生成AI（GenAI）预计会在不同的产业和学术领域取得惊人的突破。在这篇论文中，我们使用DDPM作为一种state-of-the-art的生成模型，对无线通信中的报文排序进行概率排序。即使报文的几何结构由网络标准决定，但概率排序仍可以提高信息率和通信性能，通过设计报文符号的概率出现的预测。与传统方法不同的是，我们利用DDPM的“混叠和生成”特点，将报文符号从噪声中学习生成，“模拟”接收器在重建报文符号时的过程。这样做的目的是使报文符号由发送器发送，并由接收器重建的符号变得非常相似。我们的仿真结果表明，我们的方案在低SNR情况下和非泊峰噪声下具有更高的网络鲁棒性和robust out-of-distribution性，同时与DNN基本标准相比，实现了3倍的约束信息增加。特别是，对64-QAM几何来说，与DNN基本标准相比，我们的方案实现了3倍的约束信息增加。”
Enhancing Near-Field Sensing and Communications with Sparse Arrays: Potentials, Challenges, and Emerging Trends
For: The paper is written for exploring the potential of extremely large-scale (XL)-arrays for overcoming the severe path loss in millimeter-wave (mmWave) and TeraHertz (THz) channels, which is crucial for enabling 6G.* Methods: The paper uses the spherical-wave (SW) model to accurately represent the near-field propagation characteristics of XL-arrays, which significantly increases signal processing complexity.* Results: The paper identifies the potential benefits of near-field sensing and communications (S&C) enabled by XL-arrays, including improving communication multiplexing capability, spatial resolution, and degrees of freedom. Additionally, the paper proposes sparse arrays (SAs) as a solution to overcome the limitations of existing XL-arrays with dense uniform array layouts, and explores the potential of XL-SAs for mmWave/THz systems using multi-subarray designs.Abstract
As a promising technique, extremely large-scale (XL)-arrays offer potential solutions for overcoming the severe path loss in millimeter-wave (mmWave) and TeraHertz (THz) channels, crucial for enabling 6G. Nevertheless, XL-arrays introduce deviations in electromagnetic propagation compared to traditional arrays, fundamentally challenging the assumption with the planar-wave model. Instead, it ushers in the spherical-wave (SW) model to accurately represent the near-field propagation characteristics, significantly increasing signal processing complexity. Fortunately, the SW model shows remarkable benefits on sensing and communications (S\&C), e.g., improving communication multiplexing capability, spatial resolution, and degrees of freedom. In this context, this article first overviews hardware/algorithm challenges, fundamental potentials, promising applications of near-field S\&C enabled by XL-arrays. To overcome the limitations of existing XL-arrays with dense uniform array layouts and improve S\&C applications, we introduce sparse arrays (SAs). Exploring their potential, we propose XL-SAs for mmWave/THz systems using multi-subarray designs. Finally, several applications, challenges and resarch directions are identified.
摘要
为了实现6G，极大规模（XL）阵列技术具有很大的潜在应用前景。然而，XL阵列引入了电磁波传播方面的偏差，从传统阵列假设中带来了挑战。为了更 accurately 表示近场传播特性，我们需要用圆波（SW）模型来取代平面波模型。这种模型具有优化沟通多路复用能力、空间分辨率和自由度等优点，因此在感知和通信（S\&C）方面具有极大的潜力。在这篇文章中，我们首先介绍硬件/算法挑战、基本潜力和XL阵列在感知和通信方面的应用潜力。然后，我们提出了使用多子阵列设计的稀疏阵列（SA）来解决现有XL阵列的局限性。最后，我们详细介绍了一些应用、挑战和研究方向。
Denoising Diffusion Probabilistic Models for Hardware-Impaired Communications
results: 我们的结果显示，与深度神经网络（DNN）接收器相比，我们的架构可以在AWGN和非高斯噪声场景下提供30%和20%的重建性能提升。Abstract
Generative AI has received significant attention among a spectrum of diverse industrial and academic domains, thanks to the magnificent results achieved from deep generative models such as generative pre-trained transformers (GPT) and diffusion models. In this paper, we explore the applications of denoising diffusion probabilistic models (DDPMs) in wireless communication systems under practical assumptions such as hardware impairments (HWI), low-SNR regime, and quantization error. Diffusion models are a new class of state-of-the-art generative models that have already showcased notable success with some of the popular examples by OpenAI and Google Brain. The intuition behind DDPM is to decompose the data generation process over small "denoising" steps. Inspired by this, we propose using denoising diffusion model-based receiver for a practical wireless communication scheme, while providing network resilience in low-SNR regimes, non-Gaussian noise, different HWI levels, and quantization error. We evaluate the reconstruction performance of our scheme in terms of bit error rate (BER) and mean-squared error (MSE). Our results show that 30% and 20% improvement in BER could be achieved compared to deep neural network (DNN)-based receivers in AWGN and non-Gaussian scenarios, respectively.
摘要
优化�ulsion AI在多样化的工业和学术领域中受到了广泛的关注，主要归功于深度生成模型，如生成预训练变换器（GPT）和扩散模型。在这篇论文中，我们探讨了在实际假设下，如硬件缺陷（HWI）、低信号强度（SNR）和量化误差的情况下，扩散模型在无线通信系统中的应用。扩散模型是一种新的生成模型，它已经在一些著名的例子中表现出了卓越的成绩。扩散模型的基本思想是将数据生成过程分解成小“净化”步骤。我们提议使用扩散模型来实现无线通信协议，并在低SNR情况下、非泊射噪、不同HWI水平和量化误差情况下提供网络可悟性。我们根据BER和MSE来评估我们的方案的重建性能。我们的结果显示，相比于深度神经网络（DNN）基于 receiver，我们的方案在AWGN和非泊射噪情况下可以提高30%和20%的BER。
Robust IRS-Element Activation for Energy Efficiency Optimization in IRS-Assisted Communication Systems With Imperfect CSI
results: 论文提出了一种基于动态程序（DP）的算法，可以在O（L log L）的复杂度下达到全局最大值，其中L是IRS元素的数量。此外，论文还提出了一种卷积relaxation-based方法（CRBM）来获得一个可行（不优化）解决方案，其复杂度为O（L^3.5）。numerical simulations表明，提出的算法比枚举搜索更快速，可以满足实际应用中的扩展性。Abstract
In this paper, we study an intelligent reflecting surface (IRS)-aided communication system with single-antenna transmitter and receiver, under imperfect channel state information (CSI). More specifically, we deal with the robust selection of binary (on/off) states of the IRS elements in order to maximize the worst-case energy efficiency (EE), given a bounded CSI uncertainty, while satisfying a minimum signal-to-noise ratio (SNR). In addition, we consider not only continuous but also discrete IRS phase shifts. First, we derive closed-form expressions of the worst-case SNRs, and then formulate the robust (discrete) optimization problems for each case. In the case of continuous phase shifts, we design a dynamic programming (DP) algorithm that is theoretically guaranteed to achieve the global maximum with polynomial complexity $O(L\,{\log L})$, where $L$ is the number of IRS elements. In the case of discrete phase shifts, we develop a convex-relaxation-based method (CRBM) to obtain a feasible (sub-optimal) solution in polynomial time $O(L^{3.5})$, with a posteriori performance guarantee. Furthermore, numerical simulations provide useful insights and confirm the theoretical results. In particular, the proposed algorithms are several orders of magnitude faster than the exhaustive search when $L$ is large, thus being highly scalable and suitable for practical applications. Moreover, both algorithms outperform a baseline scheme, namely, the activation of all IRS elements.
摘要
在这篇论文中，我们研究了一个智能反射表面（IRS）干支持的通信系统，其中传输机和接收机各只有一个天线。我们更加特别地关注在不完全的通道状态信息（CSI）下，对IRS元素的二进制（On/Off）状态的精度选择，以最大化最差情况的能量效率（EE），同时保证最小化噪声比率（SNR）。此外，我们不仅考虑了连续的IRS频率偏移，还考虑了离散的IRS频率偏移。首先，我们 deriv出了最差情况的SNR的关闭式表达，然后将问题转化为一个稳定优化问题。在连续频率偏移的情况下，我们设计了一个动态规划算法（DP），该算法在规模为$L$的IRS元素下可以在$O(L\log L)$的复杂度下实现全局最大化。在离散频率偏移的情况下，我们开发了一种基于凸 relaksation 的方法（CRBM），可以在$O(L^{3.5})$的复杂度下获得一个可行（不优）解。numerical simulations 表明，提议的算法在大$L$时速度是几个数量级快于枚举搜索，因此具有高可扩展性和实际应用中的适用性。此外，两种算法都超过了一个基eline scheme，即所有IRS元素的活动。
Novel Expressions for the Outage Probability and Diversity Gains in Fluid Antenna System
results: 通过一种新的准确匹配方法，我们 derivated一个简单又准确的关闭形式的近似，用于MGC-FAS schemes的OP性能。这个近似是通过两个阶段来完成：首先，对每个MGC-FAS支路的CDF进行近似，然后对MGC-FAS scheme的总体CDF进行近似。通过这些结果，我们得到了关闭形式的OP和极限OP。最后，我们的近似被 validate by numerical results，并在不同的多样性FAS场景下 demonstarte了其准确性。Abstract
The flexibility and reconfigurability at the radio frequency (RF) front-end offered by the fluid antenna system (FAS) make this technology promising for providing remarkable diversity gains in networks with small and constrained devices. Toward this direction, this letter compares the outage probability (OP) performance of non-diversity and diversity FAS receivers undergoing spatially correlated Nakagami-$m$ fading channels. Although the system properties of FAS incur in complex analysis, we derive a simple yet accurate closed-form approximation by relying on a novel asymptotic matching method for the OP of a maximum-gain combining-FAS (MGC-FAS). The approximation is performed in two stages, the approximation of the cumulative density function (CDF) of each MGC-FAS branch, and then the approximation of the end-to-end CDF of the MGC-FAS scheme. With these results, closed-form expressions for the OP and the asymptotic OP are derived. Finally, numerical results validate our approximation of the MGC-FAS scheme and demonstrate its accuracy under different diversity FAS scenarios.
摘要
这个流动天线系统（Fluid Antenna System，FAS）的灵活性和重新配置能力使得这技术在有限的设备下提供了杰出的多标的优势。以这个方向为目标，这封信件比较了非多标和多标FAS接收器在空间相关的 Nakagami-$m$ 折射通道上的失败几率（OP）性能。即使FAS系统具有复杂的系统特性，我们靠一种新的概念匹配方法 derivation 了一个简单又准确的关键值分布函数（CDF）的扩展。这个扩展是在两个阶段进行的：首先，对每个最大增幅Combining-FAS（MGC-FAS）分支的CDF进行扩展，然后对MGC-FAS的终端CDF进行扩展。从这些结果，我们得到了关于OP和杰出OP的关键表达式。最后，我们的扩展验证了MGC-FAS的精度，并在不同的多标FAS情况下进行了数值验证。
IHT-Inspired Neural Network for Single-Snapshot DOA Estimation with Sparse Linear Arrays
results: 实验结果表明，提议的方法可以保持强的解释性，并且在全面阵列信号重建和单拍 snapshot DOA估计中显示出更高的准确率和更快的收敛速率。Abstract
Single-snapshot direction-of-arrival (DOA) estimation using sparse linear arrays (SLAs) has gained significant attention in the field of automotive MIMO radars. This is due to the dynamic nature of automotive settings, where multiple snapshots aren't accessible, and the importance of minimizing hardware costs. Low-rank Hankel matrix completion has been proposed to interpolate the missing elements in SLAs. However, the solvers of matrix completion, such as iterative hard thresholding (IHT), heavily rely on expert knowledge of hyperparameter tuning and lack task-specificity. Besides, IHT involves truncated-singular value decomposition (t-SVD), which has high computational cost in each iteration. In this paper, we propose an IHT-inspired neural network for single-snapshot DOA estimation with SLAs, termed IHT-Net. We utilize a recurrent neural network structure to parameterize the IHT algorithm. Additionally, we integrate shallow-layer autoencoders to replace t-SVD, reducing computational overhead while generating a novel optimizer through supervised learning. IHT-Net maintains strong interpretability as its network layer operations align with the iterations of the IHT algorithm. The learned optimizer exhibits fast convergence and higher accuracy in the full array signal reconstruction followed by single-snapshot DOA estimation. Numerical results validate the effectiveness of the proposed method.
摘要
单一快照方向估测（DOA）预测使用稀疏线性阵列（SLAs）在汽车多元追踪领域获得了重要的注意力。这是因为汽车设置的动态性，不能取得多个快照，并且优先运算成本。低维汉宝网络完成（Low-rank Hankel matrix completion）已经建议来 interpolate 缺失的元素 в SLAs。然而，对矩阵完成的解决方案，如循环几何���（IHT），需要专家知识来调整参数，而且lacks task-specificity。此外，IHT 包含 truncated-singular value decomposition（t-SVD），它在每个迭代中有高的计算成本。在本文中，我们提出了一个 IHT 灵感的神经网络，称为 IHT-Net。我们使用了循环神经网络结构来实现 IHT 算法的实现。此外，我们将浅层自动化网络与 t-SVD 结合，从而降低计算成本，并生成了一个新的优化器通过监督学习。IHT-Net 维持了强大的解释性，因为其网络层操作与 IHT 算法的迭代相似。学习的优化器展示了快速的融合和更高的精度在全阵列信号重建和单快照 DOA 预测中。 numerics validate the effectiveness of the proposed method.
A Simple Method for the Performance Analysis of Fluid Antenna Systems under Correlated Nakagami-$m$ Fading
for: investigate the performance of a single-antenna fluid antenna system (FAS) over spatially correlated Nakagami-$m$ fading channels
methods: employ a novel asymptotic matching method to obtain simple and highly accurate closed-form approximations for the cumulative density function of the FAS channel and the outage probability of the proposed system
results: the proposed approximations are validated, and it is shown that the FAS can meet or even exceed the performance attained by the conventional maximal ratio combining (MRC) technique.Here are the three points in English for reference:
for: investigate the performance of a single-antenna fluid antenna system (FAS) over spatially correlated Nakagami-$m$ fading channels
methods: employ a novel asymptotic matching method to obtain simple and highly accurate closed-form approximations for the cumulative density function of the FAS channel and the outage probability of the proposed system
results: the proposed approximations are validated, and it is shown that the FAS can meet or even exceed the performance attained by the conventional maximal ratio combining (MRC) technique.Abstract
By recognizing the tremendous flexibility of the emerging fluid antenna system (FAS), which allows dynamic reconfigurability of the location of the antenna within a given space, this paper investigates the performance of a single-antenna FAS over spatially correlated Nakagami-$m$ fading channels. Specifically, simple and highly accurate closed-form approximations for the cumulative density function of the FAS channel and the outage probability of the proposed system are obtained by employing a novel asymptotic matching method, which is an improved version of the well-known moment matching. With this method, the outage probability can be computed {simply} without incurring complex multi-fold integrals, thus requiring negligible computational effort. Finally, the accuracy of the proposed approximations is validated, and it is shown that the FAS can meet or even exceed the performance attained by the conventional maximal ratio combining (MRC) technique.
摘要
通过认可emerging fluid antenna system（FAS）的巨大的灵活性，这篇论文研究了单antenna FAS在 Nakagami-$m$ 抖抖几何渠道上的性能。特别是，通过一种新的增强版matching方法，提出了一种简单高度准确的闭式函数方法，可以计算FAS通道的总概率分布和提携系统的失业率。这种方法不需要进行复杂的多重积分，因此计算工作量很低。最后，研究证明了提携系统的性能可以达到或超过 convential maximal ratio combining（MRC）技术的性能。
Channel Estimation in Underdetermined Systems Utilizing Variational Autoencoders
results: 这个研究展示了将VAE应用到UD系统中，可以获得非常好的性能，并且不需要完美的CSI During the training phase。这与大多数深度学习（DL）基本的CE方法不同，这些方法通常需要完美的CSI During the training phase。Abstract
In this work, we propose to utilize a variational autoencoder (VAE) for channel estimation (CE) in underdetermined (UD) systems. The basis of the method forms a recently proposed concept in which a VAE is trained on channel state information (CSI) data and used to parameterize an approximation to the mean squared error (MSE)-optimal estimator. The contributions in this work extend the existing framework from fully-determined (FD) to UD systems, which are of high practical relevance. Particularly noteworthy is the extension of the estimator variant, which does not require perfect CSI during its offline training phase. This is a significant advantage compared to most other deep learning (DL)-based CE methods, where perfect CSI during the training phase is a crucial prerequisite. Numerical simulations for hybrid and wideband systems demonstrate the excellent performance of the proposed methods compared to related estimators.
摘要
在这项工作中，我们提议使用变量自动编码器（VAE）来进行通道估计（CE）在不充分掌握（UD）系统中。这种方法的基础是一个最近提出的概念，在该概念中，VAE被训练在通道状态信息（CSI）数据上，并用来参数化MSE优化的参数。本文的贡献在扩展现有框架从完全掌握（FD）系统中扩展到UD系统中，这些系统在实践中具有非常高的重要性。特别值得一提的是，该估计变体不需要在训练阶段获得完美的CSI，这与大多数深度学习（DL）基于CE方法不同，这些方法通常需要在训练阶段获得完美的CSI。数字实验室中的干扰和宽频系统的数据显示了我们提议的方法的优秀性相比于相关的估计器。
Bayes-Optimal Estimation in Generalized Linear Models via Spatial Coupling
results: 研究发现，使用spatially coupled design和AMP算法可以实现高维度下的MSE接近 asymptotic MMSE。此外， numerically Results for phase retrieval和rectified linear regression also show that spatially coupled designs can achieve lower MSE than i.i.d. Gaussian designs at finite dimensions when used with AMP algorithms.Abstract
We consider the problem of signal estimation in a generalized linear model (GLM). GLMs include many canonical problems in statistical estimation, such as linear regression, phase retrieval, and 1-bit compressed sensing. Recent work has precisely characterized the asymptotic minimum mean-squared error (MMSE) for GLMs with i.i.d. Gaussian sensing matrices. However, in many models there is a significant gap between the MMSE and the performance of the best known feasible estimators. In this work, we address this issue by considering GLMs defined via spatially coupled sensing matrices. We propose an efficient approximate message passing (AMP) algorithm for estimation and prove that with a simple choice of spatially coupled design, the MSE of a carefully tuned AMP estimator approaches the asymptotic MMSE in the high-dimensional limit. To prove the result, we first rigorously characterize the asymptotic performance of AMP for a GLM with a generic spatially coupled design. This characterization is in terms of a deterministic recursion (`state evolution') that depends on the parameters defining the spatial coupling. Then, using a simple spatially coupled design and judicious choice of functions defining the AMP, we analyze the fixed points of the resulting state evolution and show that it achieves the asymptotic MMSE. Numerical results for phase retrieval and rectified linear regression show that spatially coupled designs can yield substantially lower MSE than i.i.d. Gaussian designs at finite dimensions when used with AMP algorithms.
摘要
我们考虑一个泛化线性模型（GLM）的问题。 GLM 包括了许多线性回传问题的 canonical 问题，例如线性回传、相位抽取和1位压缩感知。 现有的工作已经精确地定义 GLM 的 asymptotic minimum mean-squared error（MMSE）。然而，在许多模型中，存在一个明显的差距 между MMSE 和最好的可行的 estimator 的性能。在这个工作中，我们解决这个问题，通过考虑 GLM 是通过 spatially coupled sensing matrices 定义的。我们提出了一个高效的approximate message passing（AMP）算法来进行估计，并证明在高维度上，对于一个简单的选择的 spatially coupled 设计，AMP 估计器的MSE接近 asymptotic MMSE。在证明这个结果时，我们首先正确地描述了 GLM 的 asymptotic performance，它是一个 deterministic recursion（state evolution），它取决于定义 spatial coupling 的参数。然后，我们使用一个简单的 spatially coupled 设计，以及judicious 选择的函数来定义 AMP，并分析fixed points 的 resulted state evolution，并证明它实现 asymptotic MMSE。numerical results 表明，在相位抽取和线性回传 regression 中，使用 spatially coupled 设计可以在finite dimensions 下，对于 AMP 算法而言，实现较低的MSE。
Investigation of mmWave Radar Technology For Non-contact Vital Sign Monitoring
paper_authors: Steven Marty, Federico Pantanella, Andrea Ronco, Kanika Dheman, Michele Magno
for: 非接触式生命 Parameters监测
methods: millimeter-wave (mmWave) 雷达技术
results: 120 GHz 雷达系统在人体测量中表现最佳，可以准确地识别心跳和呼吸活动。Abstract
Non-contact vital sign monitoring has many advantages over conventional methods in being comfortable, unobtrusive and without any risk of spreading infection. The use of millimeter-wave (mmWave) radars is one of the most promising approaches that enable contact-less monitoring of vital signs. Novel low-power implementations of this technology promise to enable vital sign sensing in embedded, battery-operated devices. The nature of these new low-power sensors exacerbates the challenges of accurate and robust vital sign monitoring and especially the problem of heart-rate tracking. This work focuses on the investigation and characterization of three Frequency Modulated Continuous Wave (FMCW) low-power radars with different carrier frequencies of 24 GHz, 60 GHz and 120 GHz. The evaluation platforms were first tested on phantom models that emulated human bodies to accurately evaluate the baseline noise, error in range estimation, and error in displacement estimation. Additionally, the systems were also used to collect data from three human subjects to gauge the feasibility of identifying heartbeat peaks and breathing peaks with simple and lightweight algorithms that could potentially run in low-power embedded processors. The investigation revealed that the 24 GHz radar has the highest baseline noise level, 0.04mm at 0{\deg} angle of incidence, and an error in range estimation of 3.45 +- 1.88 cm at a distance of 60 cm. At the same distance, the 60 GHz and the 120 GHz radar system shows the least noise level, 0.0lmm at 0{\deg} angle of incidence, and error in range estimation 0.64 +- 0.01 cm and 0.04 +- 0.0 cm respectively. Additionally, tests on humans showed that all three radar systems were able to identify heart and breathing activity but the 120 GHz radar system outperformed the other two.
摘要
非接触生命 Parameters 监测有多种优点，包括舒适、不侵入和不扩散感染。使用毫米波（mmWave）雷达是一种非接触监测生命 Parameters 的非常有前途的方法之一。新的低功耗实现技术可能使生命 Parameters 监测在嵌入式、电池操作的设备中进行。这些新的低功耗传感器增加了精准和可靠地生命 Parameters 监测的挑战，特别是心率跟踪问题。这项工作将关注三种频率变化连续波（FMCW）低功耗雷达的调查和特征化。这三种雷达的各自的载波频率分别为24GHz、60GHz和120GHz。测试平台首先在模拟人体的phantom模型上进行测试，以准确评估基准噪声、误差范围估计和位移估计误差。此外，系统还被用来收集来自三名人类试验者的数据，以评估可能通过简单和轻量级算法在低功耗嵌入式处理器中识别心跳和呼吸活动的可能性。调查表明，24GHz雷达具有最高的基准噪声水平（0.04mm，0°角度），误差范围估计为60cm处的3.45±1.88cm。相比之下，60GHz和120GHz雷达系统在同一个距离处具有最低的基准噪声水平（0.01mm，0°角度）和误差范围估计（0.64±0.01cm和0.04±0.0cm）。此外，对人类试验者进行测试表明，所有三种雷达系统都能够识别心跳和呼吸活动，但120GHz雷达系统表现最佳。
User Power Measurement Based IRS Channel Estimation via Single-Layer Neural Network
results: 比对 existed 力量测量基础的设计，NN enabled IRS通道估计方法可以显著提高多用户通信系统中 minimum received signal-to-noise ratio（SNR）。Abstract
One main challenge for implementing intelligent reflecting surface (IRS) aided communications lies in the difficulty to obtain the channel knowledge for the base station (BS)-IRS-user cascaded links, which is needed to design high-performance IRS reflection in practice. Traditional methods for estimating IRS cascaded channels are usually based on the additional pilot signals received at the BS/users, which increase the system training overhead and also may not be compatible with the current communication protocols. To tackle this challenge, we propose in this paper a new single-layer neural network (NN)-enabled IRS channel estimation method based on only the knowledge of users' individual received signal power measurements corresponding to different IRS random training reflections, which are easily accessible in current wireless systems. To evaluate the effectiveness of the proposed channel estimation method, we design the IRS reflection for data transmission based on the estimated cascaded channels in an IRS-aided multiuser communication system. Numerical results show that the proposed IRS channel estimation and reflection design can significantly improve the minimum received signal-to-noise ratio (SNR) among all users, as compared to existing power measurement based designs.
摘要
一个主要挑战在实现智能反射表（IRS）协助通信是获取BS-IRS-用户层次链的通道知识，这是实际设计高性能IRS反射的必要前提。传统的IRS层次通道估算方法通常基于BS/用户收到的额外测试信号，这会增加系统训练负担并且可能与当前通信协议不兼容。为解决这个挑战，我们在本纸提出了一种基于单层神经网络（NN）的IRS通道估算方法，只需要用户个人接收信号强度测量对应不同的IRS随机测试反射，这些测量值易于获取现有无线系统中。为评估提案的效果，我们设计了基于估算的IRS反射 для数据传输在IRS协助多用户通信系统中。 numerically results show that the proposed IRS channel estimation and reflection design can significantly improve the minimum received signal-to-noise ratio（SNR） among all users, as compared to existing power measurement based designs.Note: "BS" stands for "base station", "IRS" stands for "intelligent reflecting surface", and "用户" stands for "user".
Trajectory Tracking Control of UAV Bicopter using Linear Quadratic Gaussian
results: 实验结果显示，LQG控制器能够成功地击倒对UAV Bicopter的惯性干扰，并且在轨迹追踪时有较低的root mean square error（RMSE）值。Abstract
This paper describes the design of a linear quadratic gaussian (LQG) for trajectory tracking control of UAV Bicopter. In this work, disturbance in the form of payload significantly affects the trajectory tracking control process on the UAV Bicopter when using a linear quadratic regulator (LQR) controller. The use of a LQR control will be optimal in the case of a state regulator towards an equilibrium point in a system, but for the tracking case, the LQR controller is not capable of optimally, especially in systems that have high levels of nonlinearity and system dynamic changes such as inertial disturbances. Therefore, this paper proposes the design of a LQG control that is expected to overcome system dynamic changes, in this case in the form of inertial disturbances to the UAV Bicopter when carrying a payload. The success of LQG control was tested in two scenarios, the first trajectory tracking at a circular position and the second with the position of the trajectory number "8". The simulation results show that the proposed LQG controller successfully overcame inertial disturbances when the UAV Bicopter performs trajectory tracking. When given an inertial disturbance, the trajectory tracking test results show that the LQG control has a lower root mean square error (RMSE) value than the LQR control.
摘要
Attitude Control and Low Cost Design of UAV Bicopter
results: 在实验床上，这个控制系统能够成功地控制几copter的姿态，并且具有耐风吟噪的性能。Abstract
This paper present a control system for the attitude and low cost design of a Bicopter. The control system uses a PID controller that receives feedback from an IMU to calculate control inputs that adjust the Bicopters attitude (roll, pitch and yaw angles) which is resistant to disturbances (wind noise) on a test bed. The control system is implemented on a hardware platform consisting of a Bicopter, an IMU sensor, and a microcontroller with low cost design. In mechanical design, the Bicopter is designed to more closely resemble the letter "V" so that the distribution of the centre of mass (CoM) of the Bicopter can be such that the servomotor torque reaction is parallel to the axis of rotation of the Bicopter during the movement of the pitch angle attitude. In electronic design, the Bicopter was developed using the ATmega328P microcontroller.
摘要
这篇论文介绍了一种控制系统，用于控制飞行器的姿态和低成本设计。该控制系统使用PID控制器，通过接收IMU传感器的反馈，计算控制输入，以调整飞行器的姿态（滚、平、旋角），抗抗扰（风噪）。控制系统在硬件平台上实现，包括飞行器、IMU传感器和微控制器，并采用低成本设计。在机械设计方面，飞行器设计更加接近字母"V"的形状，以使得飞行器的中心质量（CoM）的分布可以使服控轴向量和飞行器的旋转轴重叠。在电子设计方面，飞行器使用ATmega328P微控制器开发。
Privacy-Aware Joint Source-Channel Coding for image transmission based on Disentangled Information Bottleneck
paper_authors: Lunan Sun, Caili Guo, Mingzhe Chen, Yang Yang for: 这个论文是针对隐私考虑的共同源通道编码（JSCC）进行研究，以避免在传输过程中泄露私人资讯。methods: 这个方法基于分离的信息瓶颈（DIB）来实现隐私考虑，具体来说是利用分离的信息瓶颈目标来分离私人资讯和公共资讯。results: 实验结果显示，这个方法可以对私人资讯的泄露率进行下降，相比之前的方法可以降低私人资讯的泄露率高达20%。Abstract
Current privacy-aware joint source-channel coding (JSCC) works aim at avoiding private information transmission by adversarially training the JSCC encoder and decoder under specific signal-to-noise ratios (SNRs) of eavesdroppers. However, these approaches incur additional computational and storage requirements as multiple neural networks must be trained for various eavesdroppers' SNRs to determine the transmitted information. To overcome this challenge, we propose a novel privacy-aware JSCC for image transmission based on disentangled information bottleneck (DIB-PAJSCC). In particular, we derive a novel disentangled information bottleneck objective to disentangle private and public information. Given the separate information, the transmitter can transmit only public information to the receiver while minimizing reconstruction distortion. Since DIB-PAJSCC transmits only public information regardless of the eavesdroppers' SNRs, it can eliminate additional training adapted to eavesdroppers' SNRs. Experimental results show that DIB-PAJSCC can reduce the eavesdropping accuracy on private information by up to 20\% compared to existing methods.
摘要
当前的隐私意识敏感的联源渠道编码（JSCC）技术目的是避免在敌对训练JSCC编码器和解码器下传输私人信息。然而，这些方法会增加额外的计算和存储需求，因为需要训练多个神经网络来满足不同的伪造者的信号噪比（SNR）来确定传输的信息。为了解决这个挑战，我们提出了一种基于分离信息瓶颈（DIB）的隐私意识敏感JSCC技术，即DIB-PAJSCC。特别是，我们 derivated一个新的分离信息瓶颈目标函数，用于分离私人信息和公共信息。在给定的私人信息和公共信息之后，发送者可以只将公共信息发送到收件人，以最小化重建误差。由于DIB-PAJSCC只发送公共信息，不需要根据伪造者的SNR进行额外的训练。实验结果表明，DIB-PAJSCC可以将伪造者在私人信息上的抓取精度降低到20%以上。
Message Passing-Based Joint Channel Estimation and Signal Detection for OTFS with Superimposed Pilots
results: 提出了一种具有减少计算复杂性的SP-DD-D接收器，可以有效减少射频信号的功率和几乎不产生频率域信号强度的减少，同时保持频率域信号强度准确性。Abstract
Receivers with joint channel estimation and signal detection using superimposed pilots (SP) can achieve high transmission efficiency in orthogonal time frequency space (OTFS) systems. However, existing receivers have high computational complexity, hindering their practical applications. In this work, with SP in the delay-Doppler (DD) domain and the generalized complex exponential (GCE) basis expansion modeling (BEM) for channels, a message passing-based SP-DD iterative receiver is proposed, which drastically reduces the computational complexity while with marginal performance loss, compared to existing ones. To facilitate channel estimation (CE) in the proposed receiver, we design pilot signal to achieve pilot power concentration in the frequency domain, thereby developing an SP-DD-D receiver that can effectively reduce the power of the pilot signal and almost no loss of CE accuracy. Extensive simulation results are provided to demonstrate the superiority of the proposed SP-DD-D receiver.
摘要
接收器使用共同频率预估和信号检测使用超出练习器（SP）可以在orthogonal时频空间（OTFS）系统中实现高传输效率。然而，现有的接收器具有高计算复杂性，使其在实际应用中受限。在这种工作中，我们使用SP在延迟-Doppler（DD）域和通用复杂幂（GCE）基础函数扩展模型（BEM）来模型通道。我们提出了一种基于消息传递的SP-DD迭代接收器，可以减少计算复杂性，与现有的接收器相比，减少性能损失。为便channel estimation（CE）在提议的接收器中，我们设计了射频信号，以实现频率域中射频信号的集中，并开发了SP-DD-D接收器，可以有效减少射频信号的输力，并且几乎没有CE精度损失。我们在实验结果中提供了广泛的示范，以证明提议的SP-DD-D接收器的优越性。
TransMUSIC: A Transformer-Aided Subspace Method for DOA Estimation with Low-Resolution ADCs
results: numerics results表明TransMUSIC算法在一 bits quantized data中表现出色，superior to traditional methods. 这些结果显示Transformer-based技术在DOA估计中的潜力。Abstract
Direction of arrival (DOA) estimation employing low-resolution analog-to-digital convertors (ADCs) has emerged as a challenging and intriguing problem, particularly with the rise in popularity of large-scale arrays. The substantial quantization distortion complicates the extraction of signal and noise subspaces from the quantized data. To address this issue, this paper introduces a novel approach that leverages the Transformer model to aid the subspace estimation. In this model, multiple snapshots are processed in parallel, enabling the capture of global correlations that span them. The learned subspace empowers us to construct the MUSIC spectrum and perform gridless DOA estimation using a neural network-based peak finder. Additionally, the acquired subspace encodes the vital information of model order, allowing us to determine the exact number of sources. These integrated components form a unified algorithmic framework referred to as TransMUSIC. Numerical results demonstrate the superiority of the TransMUSIC algorithm, even when dealing with one-bit quantized data. The results highlight the potential of Transformer-based techniques in DOA estimation.
摘要
direction of arrival (DOA) 估计使用低分辨率analog-to-digital convertors (ADCs) 已成为一个挑战和吸引人的问题，尤其是大规模阵列的普及。大规模的量化误差使得从量化数据中提取信号和噪声子空间变得复杂。为解决这个问题，本文提出了一种新的方法，利用Transformer模型来支持子空间估计。在这种模型中，多个快照被处理在平行进程中，使得global correlationspan它们。学习的子空间使我们能够构建MUSIC谱和使用神经网络基于峰找器来进行网格化DOA估计。此外，获得的子空间包含重要信息的模型顺序，allow us to determine the exact number of sources。这些集成的组件形成一个统一的算法框架，称为TransMUSIC。数学结果表明TransMUSIC算法在处理一比特量化数据时具有优势。结果也 highlights the potential of Transformer-based techniques in DOA estimation.
Exploration into Optimal State Estimation with Event-triggered Communication
results: 我们通过 simulation results 示出了我们的方法的效果。Abstract
This paper deals with the problem of remote estimation of the state of a discrete-time stochastic linear system observed by a sensor with computational capacity to calculate local estimates. We design an event-triggered communication (ETC) scheme and a remote state estimator to optimally calibrate the tradeoff between system performance and limited communication resources. The novel communication scheme is the time-varying thresholding version for the cumulative innovation-driven communication scheme in [1], and its transmission probability is given. We derive the corresponding remote minimum mean square error (MMSE) estimator and present a tight upper bound for its MSE matrices. We also show that by employing a couple of weak assumptions, the optimality problem becomes (asymptotically) exact and can be addressed in an Markov Decision Process (MDP) framework, which delivers optimal policy and cost in an algorithmic procedure. The simulation results illustrate the effectiveness of our approach.
摘要