paper_authors: Trung Vu, Francisco Laport, Hanlu Yang, Vince D. Calhoun, Tulay Adali
for: This paper proposes two novel methods for constrained independent vector analysis (ICA) to improve the quality of separation in multi-subject functional magnetic resonance imaging (fMRI) data analysis.
methods: The two proposed methods are based on an adaptive-reverse scheme to select variable thresholds for the constraints and a threshold-free formulation by leveraging the unique structure of IVA.
results: The proposed methods provide significantly better separation quality and model match while providing computationally efficient and highly reproducible solutions, as demonstrated through simulations and analysis of resting state fMRI data collected from 98 subjects.Abstract
Independent component analysis (ICA) is now a widely used solution for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Independent vector analysis (IVA) generalizes ICA to multiple datasets, i.e., to multi-subject data, and in addition to higher-order statistical information in ICA, it leverages the statistical dependence across the datasets as an additional type of statistical diversity. As such, it preserves variability in the estimation of single-subject maps but its performance might suffer when the number of datasets increases. Constrained IVA is an effective way to bypass computational issues and improve the quality of separation by incorporating available prior information. Existing constrained IVA approaches often rely on user-defined threshold values to define the constraints. However, an improperly selected threshold can have a negative impact on the final results. This paper proposes two novel methods for constrained IVA: one using an adaptive-reverse scheme to select variable thresholds for the constraints and a second one based on a threshold-free formulation by leveraging the unique structure of IVA. We demonstrate that our solutions provide an attractive solution to multi-subject fMRI analysis both by simulations and through analysis of resting state fMRI data collected from 98 subjects -- the highest number of subjects ever used by IVA algorithms. Our results show that both proposed approaches obtain significantly better separation quality and model match while providing computationally efficient and highly reproducible solutions.
摘要
独立组分分析（ICA）已成为多subject功能磁共振成像（fMRI）数据分析的广泛使用解决方案。独立向量分析（IVA）总结ICA，并在多个数据集之间启用统计依赖关系作为额外的统计多样性。因此，它保留单个数据集的变化，但可能在数据集的数量增加时表现不佳。受限制的IVA是一种有效的解决方案，通过包含可用的先前信息来减少计算问题并提高分离质量。现有的受限制IVA方法通常需要用户定义的阈值来定义约束。然而，不当选择的阈值可能会对最终结果产生负面影响。本文提出了两种新的受限制IVA方法：一种使用逆向的变量阈值选择约束，并一种基于阈值自由形式，通过利用IVA的特殊结构来减少计算问题。我们的研究表明，我们的解决方案可以在多subject fMRI数据中提供更好的分离质量和模型匹配，同时提供计算效率和高度可重现的解决方案。
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion
results: 作者的方法可以应用于各种矩阵结构，如汉ке尔和双汉ке尔矩阵，并且在不含噪声的情况下和噪声的情况下都有较好的表现，并且有理论保证。Abstract
In this paper, we investigate the problem of recovering the frequency components of a mixture of $K$ complex sinusoids from a random subset of $N$ equally-spaced time-domain samples. Because of the random subset, the samples are effectively non-uniform. Besides, the frequency values of each of the $K$ complex sinusoids are assumed to vary continuously within a given range. For this problem, we propose a two-step strategy: (i) we first lift the incomplete set of uniform samples (unavailable samples are treated as missing data) into a structured matrix with missing entries, which is potentially low-rank; then (ii) we complete the matrix using a weighted nuclear minimization problem. We call the method a \emph{ weighted lifted-structured (WLi) low-rank matrix recovery}. Our approach can be applied to a range of matrix structures such as Hankel and double-Hankel, among others, and provides improvement over the unweighted existing schemes such as EMaC and DEMaC. We provide theoretical guarantees for the proposed method, as well as numerical simulations in both noiseless and noisy settings. Both the theoretical and the numerical results confirm the superiority of the proposed approach.
摘要
在这篇论文中，我们研究了一个复杂的混合信号恢复问题，即从一个随机选择的 $N$ 个时域样本中恢复 $K$ 个复杂的声学信号的频率组分。由于随机选择的样本，实际上是非均匀的。此外，每个 $K$ 个声学信号的频率值假设是连续变化在给定的范围内。为解决这个问题，我们提出了一种两步策略：1. 首先，我们将缺失样本提升成一个结构化的缺失数据矩阵，这个矩阵具有缺失的元素，可能是低级的。2. 然后，我们使用一个权重 nuclear minimization 问题来完善该矩阵。我们称之为权重提升结构（WLi）低级矩阵恢复。我们的方法可以应用于各种矩阵结构，如汉KEL和双汉KEL等，并且比不Weighted existing schemes such as EMaC and DEMaC 提供更好的性能。我们提供了理论保证，以及在无噪和噪存在的情况下的数学实验。两者都证明了我们的方法的优越性。
Joint Transmit Signal and Beamforming Design for Integrated Sensing and Power Transfer Systems
paper_authors: Kenneth MacSporran Mayer, Nikita Shanin, Zhenlong You, Sebastian Lotter, Stefan Brückner, Martin Vossiek, Laura Cottatellucci, Robert Schober
results: 论文通过Grid搜索、semidefinite relaxation（SDR）和successive convex approximation（SCA）等方法解决了一个非对称优化问题，并证明了在大于平均发射功率的情况下，各个接收器的平均收集能量 monotonicity 增长 With 探测目标（ST）的扫描方向。同时，该论文还证明了ISAPT系统中感知性和能量传输之间的贸易OFF。Abstract
Integrating different functionalities, conventionally implemented as dedicated systems, into a single platform allows utilising the available resources more efficiently. We consider an integrated sensing and power transfer (ISAPT) system and propose the joint optimisation of the rectangular pulse-shaped transmit signal and the beamforming design to combine sensing and wireless power transfer (WPT) functionalities efficiently. In contrast to prior works, we adopt an accurate non-linear circuit-based energy harvesting (EH) model. We formulate a non-convex optimisation problem for a general number of EH receivers and a single sensing target (ST) and solve the problem via a grid search over the pulse duration, semidefinite relaxation (SDR), and successive convex approximation (SCA). The average harvested power is shown to monotonically increase with the pulse duration when the average transmit power budget is large. We discuss the trade-off between sensing performance and power transfer of the ISAPT system. The proposed approach significantly outperforms a heuristic baseline scheme based on a linear EH model, which linearly combines energy beamforming with the beamsteering vector in the direction to the ST as its transmit strategy.
摘要
Electromagnetic manifold characterization of antenna arrays
results: 我们的numerical result表明，使用该模型可以实现高度的 beamforming gain优化，并且可以考虑 polarization of the receive field以及radiated power density的约束。系统可以通过利用该array manifold来实现更高的 beamforming gain，相比之下使用较准的模型进行 beamforming。Abstract
Antenna behaviors such as mutual coupling, near-field propagation, and polarization cannot be neglected in signal and channel models for wireless communication. We present an electromagnetic-based array manifold that accounts for several complicated behaviors and can model arbitrary antenna configurations. We quantize antennas into a large number of Hertzian dipoles to develop a model for the radiated array field. The resulting abstraction provides a means to predict the electric field for general non-homogeneous array geometries through a linear model that depends on the point source location, the position of each Hertzian dipole, and a set of coefficients obtained from electromagnetic simulation. We then leverage this model to formulate a beamforming gain optimization that can be adapted to account for polarization of the receive field as well as constraints on the radiated power density. Numerical results demonstrate that the proposed method achieves accuracy that is close to that of electromagnetic simulations. By leveraging the developed array manifold for beamforming, systems can achieve higher beamforming gains compared to beamforming with less accurate models.
摘要
天线行为如共振、近场传播和极化不能被忽略在无线通信中的信号和通道模型中。我们提出一个电磁场基础的阵列构造，考虑到多种复杂的行为，可以模型任意天线配置。我们将天线量化为一大量的赫兹点短波天线，开发了一个模型以预测通过天线阵列的电磁场。这个抽象提供了一个可以预测通过非均匀天线配置的电磁场的线性模型，这个模型取决于天线点源位置、每个赫兹点天线的位置以及从电磁 simulations 中获得的一组系数。我们然后利用这个模型，实现了一个对极化 receive 场进行最佳化的焦点增强，并且可以根据射频场的极化和射频功率密度的限制进行最佳化。 numerics результалтати显示，提案的方法具有与电磁 simulations 的准确性相似的精度。通过利用开发的阵列构造进行焦点增强，系统可以在焦点增强方面比使用较不精度的模型取得更高的性能。
Integrated Distributed Semantic Communication and Over-the-air Computation for Cooperative Spectrum Sensing
results: 在ICC框架下，实现了一种名为ICC-CSS的特定系统，对于独立同分布的PU信号样本，理论上证明与优化探测器-相关器（E-C）探测器相等。在各种传统CSS方案中，ICC-CSS在检测性能、频率干扰抗干扰和检测器稳定性方面表现出优异性，并且可扩展性良好。Abstract
Cooperative spectrum sensing (CSS) is a promising approach to improve the detection of primary users (PUs) using multiple sensors. However, there are several challenges for existing combination methods, i.e., performance degradation and ceiling effect for hard-decision fusion (HDF), as well as significant uploading latency and non-robustness to noise in the reporting channel for soft-data fusion (SDF). To address these issues, in this paper, we propose a novel framework for CSS that integrates communication and computation, namely ICC. Specifically, distributed semantic communication (DSC) jointly optimizes multiple sensors and the fusion center to minimize the transmitted data without degrading detection performance. Moreover, over-the-air computation (AirComp) is utilized to further reduce spectrum occupation in the reporting channel, taking advantage of the characteristics of the wireless channel to enable data aggregation. Under the ICC framework, a particular system, namely ICC-CSS, is designed and implemented, which is theoretically proved to be equivalent to the optimal estimator-correlator (E-C) detector with equal gain SDF when the PU signal samples are independent and identically distributed. Extensive simulations verify the superiority of ICC-CSS compared with various conventional CSS schemes in terms of detection performance, robustness to SNR variations in both the sensing and reporting channels, as well as scalability with respect to the number of samples and sensors.
摘要
合作频率感知（CSS）是一种有前途的方法，以提高 PRIMARY USER（PU）的探测，使用多个感知器。然而，现有的组合方法存在一些挑战，例如性能下降和层次效应（HDF）的硬件决策整合，以及报告通道中的上传延迟和阈值噪声（SDF）。为了解决这些问题，在这篇论文中，我们提出了一种新的CSS框架，即ICC。具体来说，分布式 semantics 通信（DSC）与多个感知器和整合中心进行共同优化，以最小化发送的数据量，而不是影响探测性能。此外，在报告通道中使用无线计算（AirComp），以进一步减少频率占用，利用无线通信的特性来实现数据聚合。在ICC框架下，我们设计了一个具体的系统，即ICC-CSS，该系统理论上与完美的统计量子探测器（E-C）相等，当PU信号样本独立同分布时。我们对ICC-CSS与各种传统CSS方案进行了广泛的实验，并证明了其在探测性能、频率响应度、报告通道噪声等方面的优越性。
Energy-efficient Wireless Image Retrieval for IoT Devices by Transmitting a TinyML Model
results: 论文的计算表明，相比基eline方案，提议方案可以实现高的检索精度和高的能效性，达到70%的能源减少，当存储图像的数量为8或更多时。Abstract
This work considers a scenario in which an edge server collects data from Internet of Things (IoT) devices equipped with wake-up receivers. Although this procedure enables on-demand data collection, there is still energy waste if the content of the transmitted data following the wake-up is irrelevant. To mitigate this, we advocate the use of Tiny Machine Learning (ML) to enable a semantic response from the IoT devices, so they can send only semantically relevant data. Nevertheless, receiving the ML model and the ML processing at the IoT devices consumes additional energy. We consider the specific instance of image retrieval and investigate the gain brought by the proposed scheme in terms of energy efficiency, considering both the energy cost of introducing the ML model as well as that of wireless communication. The numerical evaluation shows that, compared to a baseline scheme, the proposed scheme can realize both high retrieval accuracy and high energy efficiency, which reaches up to 70% energy reduction when the number of stored images is equal to or larger than 8.
摘要
这个研究场景中，边服务器从互联网智能设备（IoT）上收集数据，这些设备配备了唤醒接收器。尽管这种方式实现了需求响应数据收集，但是如果传输的数据内容无关，则会产生能源浪费。为了解决这个问题，我们建议使用小型机器学习（ML），让IoT设备发送只有Semantically相关的数据。然而，接收ML模型和ML处理在IoT设备上占用了额外的能源。我们对具体的图像检索情况进行了研究，并评估了我们的方案在能效率方面的提升，包括引入ML模型的能源成本以及无线通信成本。数值评估显示，相比基eline方案，我们的方案可以实现高准确率和高能效率，可以达到70%的能源减少，当存储图像的数量为8或更大时。
Superimposed Chirp Waveforms for SWIPT with Diplexer-based Integrated Receivers
results: 我们通过分析和numerical计算结果显示，对于考虑的系统设计，使用抽象振荡基于SWIPT的方案可以提高平均HE性能的提高30%，提高最小级别的HE在多用户网络中，并将能量传输范围扩展到fixed-frequency波形之外。此外，我们还证明了在SWIPT应用中包括DIR接收器可以在能量传输和信息传输之间扩大能量信息传输区域，与通常考虑的力分配接收器相比。Abstract
In this paper, we present the superposition of chirp waveforms for simultaneous wireless information and power transfer (SWIPT) applications. Exploiting the chirp waveform characteristics enables us to superimpose multiple chirps, thereby allowing transmission of the same number of waveforms over less bandwidth. This enables us to perform subband selection when operating over set of orthogonal subbands. Furthermore, we consider a user equipped with a diplexer-based integrated receiver (DIR), which enables to extract radio frequency power and decode information from the same signal without splitting. Thereby, incorporating chirp superposition and subband selection, a transmission scheme is proposed to exploit both the diode's nonlinearity and frequency diversity. We derive novel closed-form analytical expressions of the average harvested energy (HE) via transmission of superimposed chirp over selected subbands based on tools from order statistics. We also analyze the downlink information rate achieved at the user. Through our analytical and numerical results, for the considered system setup, we show that superimposed chirp-based SWIPT provides an improvement of 30$\%$ in average HE performance as compared to multisine waveforms consisting of a set of fixed-frequency cosine signals, improves the minimum level of HE in a multiuser network, and extends the operating range of energy transfer as compared to fixed-frequency waveforms. Furthermore, we illustrate that the inclusion of DIR at the receiver for SWIPT enlarges the energy-information transfer region when compared to the widely considered power splitting receiver.
摘要
在这篇论文中，我们介绍了同时无线信息和能量传输（SWIPT）应用中的振荡波形superposition。利用振荡波形特点，我们可以同时传输多个振荡，从而在带宽上传输同样多个波形。这使得可以进行子带选择，当操作于多个 ortogonal subband 时。此外，我们考虑了一个装备有diplexer-based integrated receiver（DIR）的用户，该 receiver 可以从同一个信号中提取无线电频率能量和解码信息。因此，通过振荡superposition和子带选择，我们提出了利用晶体管非线性和频率多样性来实现 SWIPT 的传输方案。我们 derivated 新的关闭式分析表达式，用于计算在superimposed chirp 上选择子带的平均收集能量（HE）的表达式。我们还分析了下链信息率在用户端。通过我们的分析和数值结果，我们表明，在考虑的系统设置下，使用振荡superposition 的 SWIPT 可以提高平均 HE 性能的30%，提高多用户网络中的最低级别HE，并将能量传输范围扩展到fixed-frequency waveforms 之外。此外，我们 illustrate dassDIR 在 SWIPT 中的存在可以在比考虑power splitting receiver 的情况下扩大能量信息传输区域。
Discerning and Enhancing the Weighted Sum-Rate Maximization Algorithms in Communications
results: 本文的贡献包括：1）对WMMSE、WSR-FP和WSR-MM之间的关系进行了完整的比较研究，并 revelaed their direct correlations; 2）提出了一种新算法WSR-MM+，可以快速 converge和减少计算负担; 3）将WSR-MM+重新定义为BCA框架下的equivalent transform，并提出了一种新版本的WSR-FP+算法。numerical simulations confirm the connections between WMMSE, WSR-FP, and WSR-MM, and the efficacy of the proposed WSR-MM+ and WSR-FP+ algorithms.Abstract
Weighted sum-rate (WSR) maximization plays a critical role in communication system design. This paper examines three optimization methods for WSR maximization, which ensure convergence to stationary points: two block coordinate ascent (BCA) algorithms, namely, weighted sum-minimum mean-square error (WMMSE) and WSR maximization via fractional programming (WSR-FP), along with a minorization-maximization (MM) algorithm, WSR maximization via MM (WSR-MM). Our contributions are threefold. Firstly, we delineate the exact relationships among WMMSE, WSR-FP, and WSR-MM, which, despite their extensive use in the literature, lack a comprehensive comparative study. By probing the theoretical underpinnings linking the BCA and MM algorithmic frameworks, we reveal the direct correlations between the equivalent transformation techniques, essential to the development of WMMSE and WSR-FP, and the surrogate functions pivotal to WSR-MM. Secondly, we propose a novel algorithm, WSR-MM+, harnessing the flexibility of selecting surrogate functions in MM framework. By circumventing the repeated matrix inversions in the search for optimal Lagrange multipliers in existing algorithms, WSR-MM+ significantly reduces the computational load per iteration and accelerates convergence. Thirdly, we reconceptualize WSR-MM+ within the BCA framework, introducing a new equivalent transform, which gives rise to an enhanced version of WSR-FP, named as WSR-FP+. We further demonstrate that WSR-MM+ can be construed as the basic gradient projection method. This perspective yields a deeper understanding into its computational intricacies. Numerical simulations corroborate the connections between WMMSE, WSR-FP, and WSR-MM and confirm the efficacy of the proposed WSR-MM+ and WSR-FP+ algorithms.
摘要
带有权重的总和（WSR）最大化在通信系统设计中扮演着关键的角色。这篇论文研究了WSR最大化优化方法的三种方法：块均衡升级（BCA）算法Weighted Sum-Minimum Mean-Square Error（WMMSE）和WSR最大化via fractional programming（WSR-FP）以及幂化-最大化（MM）算法WSR最大化via MM（WSR-MM）。我们的贡献有三个方面：一、我们指出了WMMSE、WSR-FP和WSR-MM之间的直接关系，尽管在文献中广泛使用，却缺乏全面的比较研究。我们通过探究BCA和MM算法框架之间的关系，揭示了WMMSE和WSR-FP中使用的等价转换技术，以及WSR-MM中使用的代表函数的直接关系。二、我们提出了一种新的算法WSR-MM+，利用MM框架中选择函数的灵活性。WSR-MM+通过缺少当前算法中的重复矩阵 inverse 操作，significantly 降低每轮计算负担和加速收敛。三、我们将WSR-MM+转移到BCA框架中，引入一个新的等价转换，从而得到一个改进版本的WSR-FP，名为WSR-FP+。我们进一步证明了WSR-MM+可以被视为基本的梯度向量 проек 方法。这种视角带来了计算复杂度的更深刻理解。numerical simulations confirm the connections between WMMSE, WSR-FP, and WSR-MM, and demonstrate the effectiveness of the proposed WSR-MM+ and WSR-FP+ algorithms.
Cross-Domain Waveform Design for 6G Integrated Sensing and Communication
results: numerical results表明，这两种波形设计策略可以优化ISAC应用中的资料率。Abstract
Orthogonal frequency division multiplexing (OFDM) is one of the representative integrated sensing and communication (ISAC) waveforms, where sensing and communications tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for OFDM-based ISAC systems, following communication-centric and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communications according to prior knowledge of the communication channel. The remaining REs are then employed for sensing, where the sidelobe level and peak to average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics. For the sensing-centric design, a `locally' perfect auto-correlation property is ensured by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed communication-centric and sensing-centric waveform designs for ISAC applications.
摘要
阶梯频分复装多普通频率分装 (OFDM) 是一种代表性的统合感知通信 (ISAC) 波形，其中感知和通信两者通常对应不同的资源元素 (RE) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for OFDM-based ISAC systems, following communication-centric and sensing-centric criteria, respectively.For the communication-centric design, a fraction of REs are optimally allocated for communications based on prior knowledge of the communication channel, and the remaining REs are then employed for sensing, where the sidelobe level and peak to average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics.For the sensing-centric design, a `locally' perfect auto-correlation property is ensured by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed communication-centric and sensing-centric waveform designs for ISAC applications.