信号处理
查看 最近的 文章
显示 2025年07月25日， 星期五 新的列表
- [1] arXiv:2507.17966 [中文pdf, pdf, html, 其他]
-
标题： 上行链路多用户OTFS的时间和频率同步标题： Time and Frequency Synchronization for Multiuser OTFS in Uplink主题： 信号处理 (eess.SP)
本文中，我们提出了用于高速移动场景下上行多用户OTFS（MU-OTFS）系统的时域和频域同步技术。 本工作专注于准确估计和校正定时偏移（TOs）和载波频率偏移（CFOs）。 具体而言，TO估计对于在时延-时间平面上定位用户的导频信号至关重要，而CFO估计则提高了信道估计的准确性。 首先，我们提出了一种针对MU-OTFS中现有多用户导频结构的TO估计技术。 我们将该导频结构中的脉冲导频（IMP）替换为具有循环前缀的更实用的导频（PCP），称为单用户启发式PCP（SU-PCP）。 该结构使用不同的Zadoff-Chu（ZC）序列，这使得接收端可以通过相关性实现导频分离。 因此，我们引入了一种基于相关性的TO估计技术，用于上行MU-OTFS系统，利用此导频结构。 接下来，提出了一种频谱高效且实用的导频模式，其中每个用户在时延-多普勒平面上的共享导频区域内发送一个PCP，称为MU-PCP。 在接收端，第二种TO估计技术利用一组滤波器来分离不同用户的信号并准确估计它们的TOs。 随后，我们推导出一个数学阈值范围，通过在相关函数中寻找第一个主要峰值而不是仅依赖最高峰值来提高TO估计的准确性。 在使用所提出的TO估计技术之一定位接收到的用户导频信号后，我们提出的CFO估计技术将多维最大似然（ML）搜索问题转化为多个一维搜索问题。 在此技术中，我们应用了第一类切比雪夫多项式基展开模型（CPF-BEM），以有效处理在获取所有用户CFO估计时信道的时间变化。
In this paper, we propose time and frequency synchronization techniques for uplink multiuser OTFS (MU-OTFS) systems in high-mobility scenarios. This work focuses on accurately estimating and correcting timing offsets (TOs) and carrier frequency offsets (CFOs). Specifically, TO estimation is essential for locating users' pilots on the delay-time plane, while CFO estimation enhances channel estimation accuracy. First, we propose a TO estimation technique for an existing multiuser pilot structure in MU-OTFS. We replace the impulse pilot (IMP) in this pilot structure with a more practical pilot with a cyclic prefix (PCP), referred to as single-user-inspired PCP (SU-PCP). This structure employs different Zadoff-Chu (ZC) sequences, which enables pilot separation via correlation at the receiver side. Consequently, we introduce a correlation-based TO estimation technique for uplink MU-OTFS using this pilot structure. Next, a spectrally efficient and practical pilot pattern is proposed, where each user transmits a PCP within a shared pilot region on the delay-Doppler plane, referred to as MU-PCP. At the receiver, the second TO estimation technique utilizes a bank of filters to separate different users' signals and accurately estimate their TOs. Then, we derive a mathematical threshold range to enhance TO estimation accuracy by finding the first major peak in the correlation function rather than relying solely on the highest peak. After locating the received users' pilot signals using one of the proposed TO estimation techniques, our proposed CFO estimation technique reduces the multi-dimensional maximum likelihood (ML) search problem into multiple one-dimensional search problems. In this technique, we apply the Chebyshev polynomials of the first kind basis expansion model (CPF-BEM) to effectively handle the time-variations of the channel in obtaining the CFO estimates for all the users.
- [2] arXiv:2507.17982 [中文pdf, pdf, html, 其他]
-
标题： 基于超表面的流体天线：从电磁学到通信模型标题： Metasurface-based Fluid Antennas: from Electromagnetics to Communications ModelPablo Ramírez-Espinosa, Cleofás Segura-Gómez, Ángel Palomares-Caballero, F. Javier López-Martínez, David Morales-Jiménez主题： 信号处理 (eess.SP)
流体天线系统（FASs）已成为无线领域的一个热门话题，作为一种有效且简单的利用空间分集的方法。 由于物理移动辐射元件的限制，电子可重构天线正在成为FASs的实用实现方式，因为改变辐射图谱在功能上等同于物理移动设备。 然而，电子可重构天线在分析建模方面带来了挑战，通常需要全波仿真或测量来进行表征；这严重限制了对系统设计有用的理论洞察的提取。 受这些困难以及对FASs日益增长的兴趣的驱动，本文我们提出了基于超表面的FASs的完整分析模型。 具体而言，我们主张通过动态超表面天线（DMAs）实现FAS的概念，DMAs此前被提出作为多输入多输出（MIMO）系统中的阵列替代方案。 我们利用电路理论，将传统的FAS信号模型重新表述为考虑超表面固有电磁效应的导纳矩阵。 该模型通过全波仿真进行了验证，显示出良好的一致性。 我们进一步说明如何将该模型应用于标准性能分析，并提供了关键指标的闭式表达式，包括所得的信号协方差矩阵。 结果证实，实际的基于DMA的FAS可以实现与位置灵活天线的理想化实现相似的性能。
Fluid antenna systems (FASs) have become a popular topic in the wireless community as an effective yet simple means of exploiting spatial diversity. Due to the limitations of physically moving radiating elements, electronically reconfigurable antennas are emerging as practical implementations of FASs, since changing the radiation pattern is functionally equivalent to physically moving the device. However, electronically reconfigurable antennas pose a challenge in terms of analytical modeling, often requiring full-wave simulations or measurements for their characterization; this severely limits the extraction of theoretical insights useful for system design. Motivated by these difficulties and the growing interest in FASs, we propose in this paper a complete analytical model for metasurface-based embodiments of FASs. Specifically, we advocate for the implementation of the FAS concept through dynamic metasurface antennas (DMAs), hitherto proposed as array replacements in multiple-input multiple-output (MIMO) systems. We leverage circuit theory to rewrite the conventional signal model of FASs in terms of admittance matrices accounting for the electromagnetic effects inherent to metasurfaces. The model is validated with full-wave simulations, showing good agreement. We further illustrate how to apply the model for standard performance analysis, and provide closed-form expressions for key metrics, including the resulting signal covariance matrix. Results confirm that practical DMA-based FASs can achieve similar performance to that of idealized implementations of position-flexible antennas.
- [3] arXiv:2507.18035 [中文pdf, pdf, html, 其他]
-
标题： 多主动STAR-RIS辅助的协同波束成形安全一体化感知与通信标题： Multiple Active STAR-RIS-Assisted Secure Integrated Sensing and Communication via Cooperative Beamforming主题： 信号处理 (eess.SP)
本文探讨了一个由多个主动同时发射和反射可重构智能表面（STAR-RISs）增强的集成感知和通信（ISAC）网络。一个基站（BS）向多个用户提供下行通信，同时对感知目标进行探测。我们联合优化基站发射波束成形器和每个主动STAR-RIS的反射/传输系数，以在满足以下条件的情况下最大化整体通信总速率：(i) 严格的感知信干噪比（SINR）要求，(ii) 机密信息泄露的上限，以及(iii) 基站和STAR-RISs上的个体硬件和总功率约束。所得到的高度非凸程序通过一个高效的交替优化（AO）框架来解决。首先，原始公式被重新表述为一种等效但更易处理的表示，并划分为子问题。基站波束成形器通过Karush-Kuhn-Tucker（KKT）条件以闭合形式更新，而STAR-RIS的反射和传输向量则通过连续凸逼近（SCA）进行优化，产生一个随后通过半定松弛求解的半定规划。全面的仿真表明，所提出的算法在被动-RIS和单个STAR-RIS基线之上实现了显著的总速率增益，同时严格满足规定的感知和安全约束。
This paper explores an integrated sensing and communication (ISAC) network empowered by multiple active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). A base station (BS) furnishes downlink communication to multiple users while concurrently interrogating a sensing target. We jointly optimize the BS transmit beamformer and the reflection/transmission coefficients of every active STAR-RIS in order to maximize the aggregate communication sum-rate, subject to (i) a stringent sensing signal-to-interference-plus-noise ratio (SINR) requirement, (ii) an upper bound on the leakage of confidential information, and (iii) individual hardware and total power constraints at both the BS and the STAR-RISs. The resulting highly non-convex program is tackled with an efficient alternating optimization (AO) framework. First, the original formulation is reformulated into an equivalent yet more tractable representation and partitioned into subproblems. The BS beamformer is updated in closed form via the Karush-Kuhn-Tucker (KKT) conditions, whereas the STAR-RIS reflection and transmission vectors are refined through successive convex approximation (SCA), yielding a semidefinite program that is then solved via semidefinite relaxation. Comprehensive simulations demonstrate that the proposed algorithm delivers substantial sum-rate gains over passive-RIS and single STAR-RIS baselines, all the while rigorously meeting the prescribed sensing and security constraints.
- [4] arXiv:2507.18096 [中文pdf, pdf, 其他]
-
标题： GNSS直接定位估计中的多路径误差传播几何图谱标题： Geometrical portrait of Multipath error propagation in GNSS Direct Position Estimation主题： 信号处理 (eess.SP)
直接定位估计（DPE）是一种从全球导航卫星系统（GNSS）信号的交叉模糊函数（CAF）中直接估计位置、速度和时间（PVT）信息的方法，在城市环境中显著增强了接收机的鲁棒性。然而，在DPE理论背景下，对多路径误差的理论表征仍然不足。几何观测表明，DPE误差在多路径和热噪声作用下分别表现出估计偏差和方差的特性。通过几何分析扩展DPE噪声方差的理论框架，本文重点通过量化由于相对于方位角和仰角的偏心偏差引起的CAF和PVT解的偏差，来建立多路径误差的几何表示。引入了一个卫星圆周多路径偏差（SCMB）模型，融合了多个卫星信道的CAF和PVT误差。通过讨论各种多路径条件，建立了最大或最小PVT偏差的边界。通过蒙特卡洛仿真和城市峡谷测试验证了多路径几何图景的正确性。研究结果表明，最大PVT偏差取决于各个卫星信道中观察到的最大多路径误差。此外，PVT偏差随着卫星仰角增加，受CAF多路径偏差投影的影响。这为从几何角度选择DPE卫星提供了参考，强调了选择高仰角和低仰角的平衡组合以实现最佳卫星几何配置的重要性。
Direct Position Estimation (DPE) is a method that directly estimate position, velocity, and time (PVT) information from cross ambiguity function (CAF) of the GNSS signals, significantly enhancing receiver robustness in urban environments. However, there is still a lack of theoretical characterization on multipath errors in the context of DPE theory. Geometric observations highlight the unique characteristics of DPE errors stemming from multipath and thermal noise as estimation bias and variance respectively. Expanding upon the theoretical framework of DPE noise variance through geometric analysis, this paper focuses on a geometric representation of multipath errors by quantifying the deviations in CAF and PVT solutions caused by off-centering bias relative to the azimuth and elevation angles. A satellite circular multipath bias (SCMB) model is introduced, amalgamating CAF and PVT errors from multiple satellite channels. The boundaries for maximum or minimum PVT bias are established through discussions encompassing various multipath conditions. The correctness of the multipath geometrical portrait is confirmed through both Monte Carlo simulations and urban canyon tests. The findings indicate that the maximum PVT bias depends on the largest multipath errors observed across various satellite channels. Additionally, the PVT bias increases with satellite elevation angles, influenced by the CAF multipath bias projection. This serves as a reference for selecting DPE satellites from a geometric standpoint, underscoring the importance of choosing a balanced combination of high and low elevation angles to achieve an optimal satellite geometry configuration.
- [5] arXiv:2507.18149 [中文pdf, pdf, 其他]
-
标题： 用于峰值功率受限IM DD系统的包络控制启用的概率整形标题： Envelope Control Enabled Probabilistic Shaping for Peak Power Constrained IM DD SystemsDongdong Zou, Wei Wang, Jiawen Yao, Zhongxing Tian, Zeyu Feng, Huan Huang, Fan Li, Gordon Ning Liu, Gangxiang Shen, Yi Cai主题： 信号处理 (eess.SP)
概率成形（PS）在强度调制和直接检测（IM-DD）系统中引起了广泛关注。 然而，由于独特的系统模型和固有的限制，PS技术在IM-DD系统中的有效应用仍然是一个开放性问题，特别是在具有记忆效应的系统中。 本文提出了一种针对峰值功率受限（PPC）IM-DD系统的新型间接PS方案。 其关键思想在于有策略地控制信号包络，以减轻由记忆引起的失真，如非线性、过冲、峰均功率比增强等。 所提出的方案在发射端结合了动态选择映射（DSLM）机制，使得当前符号不仅由当前的比特模式决定，还由指定记忆长度内之前生成的符号决定。 在接收端，提出了一种带有改进的M-BCJR算法的Turbo均衡器，以实现由DSLM引起的模糊比特的恢复。 在56GBaud PAM8系统中的实验验证表明，该方案在2km单模光纤传输中表现出1dB的接收灵敏度提升。 此外，该方案还被证明与典型的概率幅度成形架构兼容，能够实现简单且细粒度的速率自适应能力。 据我们所知，这项工作为在具有记忆效应的PPC IM-DD系统中应用PS技术开辟了新的视角。
Probabilistic shaping (PS) has attracted significant attention in intensity-modulation and direct-detection (IM-DD) systems. However, due to the unique system model and inherent constraints, the effective application of the PS technique is still an open question in IM-DD systems, particularly in systems with memory effects. In this paper, a novel indirect PS scheme tailored for peak power constrained (PPC) IM-DD systems is proposed. The key idea lies in strategically controlling the signal envelope to mitigate memory-induced impairments, such as nonlinearity, overshoot, peak-to-average power ratio enhancement, etc. The proposed scheme incorporates a dynamic selective mapping (DSLM) mechanism at the transmitter, enabling an untypical bit-to-symbol mapping in which the current symbol is not only determined by the current bits pattern but also by previously generated symbols within a specified memory length. At the receiver side, a turbo equalizer with a modified M-BCJR algorithm is proposed to achieve the recovery of ambiguous bits induced by DSLM. Experimental verification in a 56GBaud PAM8 system demonstrates that the proposed scheme exhibits 1dB receiver sensitivity improvement over 2km single-mode fiber transmission. In addition, the proposed scheme has also been demonstrated to be compatible with the typical probabilistic amplitude shaping architecture, enabling a simple and fine-granularity rate adaptation capability. To the best of our knowledge, this work opens a new sight for the application of the PS technique in PPC IM-DD systems with memory effects.
- [6] arXiv:2507.18166 [中文pdf, pdf, html, 其他]
-
标题： 通过多天线处理减轻GNSS干扰器和欺骗设备的影响标题： GNSS Jammer and Spoofer Mitigation via Multi-Antenna Processing主题： 信号处理 (eess.SP) ; 信息论 (cs.IT)
现代定位依赖于全球导航卫星系统（GNSS）的无线电信号。 它们的接收功率较低，使得这些无线电信号容易受到干扰攻击，其中恶意发射器发出强干扰以破坏信号捕获。 此外，GNSS容易受到欺骗攻击，其中恶意发射器通过传输虚假的GNSS信号来模仿合法卫星。 我们提出了SCHIEBER，一种新型的多天线GNSS接收机方法，在不需要任何接收机位置或攻击类型先验知识的情况下，能够减轻干扰器和欺骗器：在信号捕获期间，使用一种最近开发的自适应空间滤波技术来减轻干扰器。 在信号捕获后，使用一种新方法识别并拒绝欺骗器，该方法通过比较获取信号的到达方向（DoA）和伪距估计的一致性来进行测试，该测试与未知的接收机位置无关。 我们通过在欺骗和干扰攻击下对GPS L1 C/A系统的大量仿真来证明我们方法的有效性。
Modern positioning relies on radio signals from global navigation satellite systems (GNSS). Their low receive power renders these radio signals susceptible to jamming attacks, in which malicious transmitters emit strong interference to disrupt signal acquisition. Moreover, GNSS are vulnerable to spoofing attacks, in which malicious transmitters mimic legitimate satellites by transmitting spurious GNSS signals. We propose SCHIEBER, a novel method for multi-antenna GNSS receivers that mitigates jammers as well as spoofers without requiring any prior knowledge of the receiver position or attack type: Jammers are mitigated during signal acquisition using a recently developed adaptive spatial filtering technique. Spoofers are identified and rejected after signal acquisition using a novel approach that tests the consistency of acquired signals by comparing their respective direction of arrival (DoA) and pseudorange estimates in a test that is invariant with respect to the unknown receiver position. We demonstrate the efficacy of our method using extensive simulations of a GPS L1 C/A system under spoofing and jamming attacks.
- [7] arXiv:2507.18167 [中文pdf, pdf, html, 其他]
-
标题： ICWLM：通过上下文学习的多任务无线大模型标题： ICWLM: A Multi-Task Wireless Large Model via In-Context Learning主题： 信号处理 (eess.SP)
无线通信技术的快速发展，特别是大规模多输入多输出（mMIMO）和毫米波（mmWave），引入了显著的网络复杂性和计算需求。 大量研究努力通过采用深度学习（DL）方法来提高物理层性能，然而这些方法通常任务特定，并且在数据稀缺和泛化方面存在困难。 为解决这些挑战，我们提出了一种新颖的上下文无线大模型（ICWLM），这是一种专为物理层同时多任务学习设计的无线原生基础模型。 与传统方法将无线数据适应到预训练大语言模型（LLMs）不同，ICWLM直接从头开始在大规模、混合的无线数据集上进行训练。 它联合解决多个经典的物理层问题，包括多用户预编码（总速率最大化和最大最小信噪比）和信道预测。 ICWLM的一个关键创新是其利用了上下文学习（ICL），使模型能够在少量演示对的情况下适应不同的系统配置和信道条件，消除了对大量重新训练的需求。 此外，我们采用动态权重平均（DWA）算法在多任务训练过程中动态平衡各个任务损失，确保在不同目标上的高效和稳定学习。 广泛的仿真结果表明，与任务特定方法相比，ICWLM表现出具有竞争力的性能，并且在未见过的系统配置中展现出显著的泛化能力。 这项工作为开发未来无线网络的统一和自适应AI模型提供了一个有前景的范例，可能减少部署复杂性并增强智能资源管理。
The rapid evolution of wireless communication technologies, particularly massive multiple-input multiple-output (mMIMO) and millimeter-wave (mmWave), introduces significant network complexity and computational demands. Significant research efforts have been made to improve physical layer performance by resorting to deep learning (DL) methods, which, however, are usually task-specific and struggle with data scarcity and generalization. To address these challenges, we propose a novel In-Context Wireless Large Model (ICWLM), a wireless-native foundation model designed for simultaneous multi-task learning at the physical layer. Unlike conventional methods that adapt wireless data to pre-trained large language models (LLMs), ICWLM is trained directly on large-scale, mixed wireless datasets from scratch. It jointly solves multiple classical physical layer problems, including multi-user precoding (sum-rate maximization and max-min SINR) and channel prediction. A key innovation of ICWLM is its utilization of in-context learning (ICL), enabling the model to adapt to varying system configurations and channel conditions with minimal demonstration pairs, eliminating the need for extensive retraining. Furthermore, we employ the Dynamic Weight Averaging (DWA) algorithm to dynamically balance the individual task losses during multi-task training, ensuring efficient and stable learning across diverse objectives. Extensive simulation results demonstrate that ICWLM achieves competitive performance compared to task-specific methods while exhibiting remarkable generalization capabilities to unseen system configurations. This work offers a promising paradigm for developing unified and adaptive AI models for future wireless networks, potentially reducing deployment complexity and enhancing intelligent resource management.
- [8] arXiv:2507.18370 [中文pdf, pdf, html, 其他]
-
标题： 基于参数化查找表的干扰下量化信号恢复标题： Quantized Signal Recovery with Interference via Parametrized Look-Up Tables评论： 13页，18图主题： 信号处理 (eess.SP)
高效的所有数字后校正低分辨率模数转换器可以通过使用查找表（LUTs）来实现。 LUT的性能可以通过结合预期输入信号、噪声水平和干扰信号的参数模型来优化。 我们评估了三种分析估计器，以与参数化LUTs集成，特别是在低分辨率、非线性或宽带量化器的应用中。 我们还提出了几种近似方法，以提高相位移键控输入信号和线性频率调制干扰信号的估计问题的可处理性。 模拟结果验证了我们的估计器在实时情况下以高精度恢复期望输入信号的瞬时值的能力。 这包括消除由于高功率带外干扰导致前端饱和而混入期望信号带宽的谐波失真。 我们的估计器被证明在常规线性滤波技术上取得了显著的增益，同时对于输入参数的变化、非线性量化器和时变干扰源也具有鲁棒性。 对于一个量化为3位的单音输入，并使用固定12抽头模型阶数进行估计，我们实现了$>$10 dB的均方误差改进和$>$20 dBc的无杂散动态范围改进。
Efficient all-digital post-correction of low-resolution analog-to-digital converters can be achieved by using Look-Up Tables (LUTs). The performance of a LUT can be optimized by incorporating a parametric model for the expected input signal, noise level, and interference signals. We evaluate three analytical estimators for integration with parametrized LUTs, especially with applications to low-resolution, non-linear, or wideband quantizers. We also propose several approximations to improve tractability of the estimation problem for Phase-Shift Keyed input signals and Linear Frequency Modulated interference signals. Simulated results validate the ability of our estimator to recover the instantaneous value of the desired input signal in real-time with a high degree of accuracy. This includes cancellation of harmonic distortion that aliases into the desired signal bandwidth from front-end saturation due to high-power out-of-band interference. Our estimators are shown to achieve a significant gain over conventional linear-filtering techniques while also being robust to changes in input parameters, non-linear quantizers, and time-variant interference sources. For a tone input quantized to 3 bits and estimated with a fixed 12-tap model order we achieve $>$10 dB improvement in Mean Square Error and $>$20 dBc improvement in Spurious-Free Dynamic Range.
- [9] arXiv:2507.18587 [中文pdf, pdf, html, 其他]
-
标题： 一种具有自适应用户速率-功率权衡的大规模MIMO预编码基础模型标题： A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff评论： 6页，3图。被IEEE国际个人、室内和移动无线电通信会议（PIMRC）2025接收主题： 信号处理 (eess.SP) ; 人工智能 (cs.AI)
深度学习（DL）由于其能够学习传播环境的特性，已成为大规模多输入多输出（mMIMO）系统中预编码的解决方案。 然而，训练此类模型需要在部署现场具有高质量的本地数据集，这些数据集通常难以收集。 我们提出了一种基于变压器的基础模型用于mMIMO预编码，旨在最小化发射机的能量消耗，同时动态适应每个用户的数据率要求。 在相同的能量消耗下，所提出的基础模型的零样本部署显著优于零 forcing，并且在复杂度降低8倍的情况下接近加权最小均方误差性能。 为了解决数据稀缺环境中的模型适应问题，我们引入了一种数据增强方法，通过计算预训练特征提取器输出之间的余弦相似性来找到与目标分布相似的训练样本。 我们的工作通过解决数据可用性和训练复杂性的挑战，实现了基于DL的解决方案的实际应用。 此外，动态配置每个用户数据率要求的能力可以被更高级别的资源分配和调度算法利用，以更好地控制能耗效率、频谱效率和公平性。
Deep learning (DL) has emerged as a solution for precoding in massive multiple-input multiple-output (mMIMO) systems due to its capacity to learn the characteristics of the propagation environment. However, training such a model requires high-quality, local datasets at the deployment site, which are often difficult to collect. We propose a transformer-based foundation model for mMIMO precoding that seeks to minimize the energy consumption of the transmitter while dynamically adapting to per-user rate requirements. At equal energy consumption, zero-shot deployment of the proposed foundation model significantly outperforms zero forcing, and approaches weighted minimum mean squared error performance with 8x less complexity. To address model adaptation in data-scarce settings, we introduce a data augmentation method that finds training samples similar to the target distribution by computing the cosine similarity between the outputs of the pre-trained feature extractor. Our work enables the implementation of DL-based solutions in practice by addressing challenges of data availability and training complexity. Moreover, the ability to dynamically configure per-user rate requirements can be leveraged by higher level resource allocation and scheduling algorithms for greater control over energy efficiency, spectral efficiency and fairness.
新提交 (展示 9 之 9 条目 )
- [10] arXiv:2507.17917 (交叉列表自 physics.optics) [中文pdf, pdf, html, 其他]
-
标题： 模块化和自动工作流用于简化拉曼信号分析标题： Modular and Automated Workflow for Streamlined Raman Signal Analysis评论： 预印本。提交至《拉曼光谱杂志》主题： 光学 (physics.optics) ; 信号处理 (eess.SP) ; 化学物理 (physics.chem-ph)
拉曼光谱是一种用于材料表征的强大工具。 然而，为了识别和处理噪声、基线漂移和随机尖峰，需要仔细的预处理。 本文提出了一种全面的方法来生成和预处理拉曼光谱。 此外，我们描述了将Voigt峰拟合到光谱中的方法，以确定峰参数。 这些方法的有效性通过合成和真实拉曼光谱进行了演示，并在开源GitHub仓库中提供了代码。
Raman spectroscopy is a powerful tool for material characterization. However, careful preprocessing is required for the identification and handling of noise, baseline drift, and random spikes. This paper presents a comprehensive approach to generating and preprocessing Raman spectra. Additionally, we describe methods for fitting Voigt peaks to the spectrum to determine peak parameters. The effectiveness of these methods is demonstrated using both synthetic and real Raman spectra, with code provided in an open-source GitHub repository.
- [11] arXiv:2507.17950 (交叉列表自 cs.IT) [中文pdf, pdf, html, 其他]
-
标题： 基于深度学习的空域信道外推用于无蜂窝大规模MIMO标题： Deep Learning-based Position-domain Channel Extrapolation for Cell-Free Massive MIMO评论： IEEE TWC。版权2025 IEEE。个人使用此材料是允许的。对于所有其他用途，必须从IEEE获得许可，无论当前或未来任何媒体，包括重新印刷/再发布此材料用于广告或促销目的，创建新的集体作品，出售或重新分发到服务器或列表，或在其他作品中重复使用本作品的任何受版权保护的部分。主题： 信息论 (cs.IT) ; 信号处理 (eess.SP)
为减少信道获取开销，空间、时间和频域信道外推技术已被广泛研究。 在本文中，我们提出了一种基于深度学习的定位域信道外推框架（命名为PCEnet），用于无蜂窝大规模多输入多输出（MIMO）系统。 用户的位置包含重要的信道特征信息，可以大大提升信道获取的效率。 在无蜂窝大规模MIMO中，不同基站与特定用户之间的传播环境各不相同，其各自的信道互不相关，但用户的位置在所有信道中保持恒定且唯一。 在此基础上，所提出的PCEnet框架利用位置作为信道之间的桥梁，建立不同信道特征之间的映射关系，从而使用一个获取的信道来辅助其他信道的估计和反馈。 具体而言，该方法首先利用神经网络（NNs）从获得的信道中推断用户的位置。{估计的位置，通过中央处理单元（CPU）在基站之间共享}会被输入到一个NN中以设计导频符号，并与反馈信息连接到信道重建NN以重建其他信道，从而显著提升信道获取性能。 此外，我们提出了一种简化策略，在重建过程中仅使用估计的位置而不修改导频设计，从而降低延迟。 此外，我们引入了一种无需位置标签的方法，推断相对用户位置而非绝对位置，从而在定位NN训练期间无需真实位置标签。 仿真结果表明，所提出的PCEnet框架可将导频和反馈开销减少高达50%。
To reduce channel acquisition overhead, spatial, time, and frequency-domain channel extrapolation techniques have been widely studied. In this paper, we propose a novel deep learning-based Position-domain Channel Extrapolation framework (named PCEnet) for cell-free massive multiple-input multiple-output (MIMO) systems. The user's position, which contains significant channel characteristic information, can greatly enhance the efficiency of channel acquisition. In cell-free massive MIMO, while the propagation environments between different base stations and a specific user vary and their respective channels are uncorrelated, the user's position remains constant and unique across all channels. Building on this, the proposed PCEnet framework leverages the position as a bridge between channels to establish a mapping between the characteristics of different channels, thereby using one acquired channel to assist in the estimation and feedback of others. Specifically, this approach first utilizes neural networks (NNs) to infer the user's position from the obtained channel. {The estimated position, shared among BSs through a central processing unit (CPU)}, is then fed into an NN to design pilot symbols and concatenated with the feedback information to the channel reconstruction NN to reconstruct other channels, thereby significantly enhancing channel acquisition performance. Additionally, we propose a simplified strategy where only the estimated position is used in the reconstruction process without modifying the pilot design, thereby reducing latency. Furthermore, we introduce a position label-free approach that infers the relative user position instead of the absolute position, eliminating the need for ground truth position labels during the localization NN training. Simulation results demonstrate that the proposed PCEnet framework reduces pilot and feedback overheads by up to 50%.
- [12] arXiv:2507.18070 (交叉列表自 cs.RO) [中文pdf, pdf, html, 其他]
-
标题： 基于相对方位测量的模块化机器人与地标定位标题： Modular Robot and Landmark Localisation Using Relative Bearing Measurements评论： 提交至 RA-L主题： 机器人技术 (cs.RO) ; 信号处理 (eess.SP) ; 系统与控制 (eess.SY)
本文我们提出了一种模块化非线性最小二乘滤波方法，用于由独立子系统组成的系统。每个子系统的状态和误差协方差估计是独立更新的，即使相对测量同时依赖于多个子系统的状态。我们将协方差交集（CI）算法作为我们解决方案的一部分，以防止子系统相互共享估计时的信息重复计算。基于最小二乘估计的CI算法的另一种推导方式使得这种集成成为可能。我们将所提出的的方法具体应用于机器人-地标定位问题。在这个问题中，相对于移动机器人SE(2)位姿测量的静止地标位置的方位角噪声测量将机器人位姿和地标位置的估计问题耦合在一起。在随机模拟研究中，我们将所提出的模块化方法与单一联合状态滤波器进行基准比较，以阐明它们各自的权衡。在该研究中，我们还包含了所提出方法的变体，这些变体在减少通信和带宽需求的情况下实现了性能的渐进退化。
In this paper we propose a modular nonlinear least squares filtering approach for systems composed of independent subsystems. The state and error covariance estimate of each subsystem is updated independently, even when a relative measurement simultaneously depends on the states of multiple subsystems. We integrate the Covariance Intersection (CI) algorithm as part of our solution in order to prevent double counting of information when subsystems share estimates with each other. An alternative derivation of the CI algorithm based on least squares estimation makes this integration possible. We particularise the proposed approach to the robot-landmark localization problem. In this problem, noisy measurements of the bearing angle to a stationary landmark position measured relative to the SE(2) pose of a moving robot couple the estimation problems for the robot pose and the landmark position. In a randomized simulation study, we benchmark the proposed modular method against a monolithic joint state filter to elucidate their respective trade-offs. In this study we also include variants of the proposed method that achieve a graceful degradation of performance with reduced communication and bandwidth requirements.
- [13] arXiv:2507.18194 (交叉列表自 cs.IT) [中文pdf, pdf, html, 其他]
-
标题： 基于MEC的网络化ISAC系统中的隐蔽通信面向低空经济标题： Covert Communications in MEC-Based Networked ISAC Systems Towards Low-Altitude Economy主题： 信息论 (cs.IT) ; 信号处理 (eess.SP)
低空经济（LAE）是一种新兴的商业模式，它高度依赖于集成感知与通信（ISAC）、移动边缘计算（MEC）和隐蔽通信。本文研究了面向LAE的基于MEC的网络化ISAC系统中的隐蔽传输设计，其中MEC服务器协调多个接入点，同时从多个无人机（UAVs）接收计算任务，在感知区域定位目标，并在多个监视者面前维持UAVs的隐蔽传输。我们首先推导了监视者处的检测误差概率（DEP）的闭式表达式。然后，通过优化通信、感知和计算资源以及UAV轨迹，制定一个总能耗最小化问题，受制于MEC服务质量的要求、DEP、雷达信干噪比以及UAV轨迹的因果性。提出了一种基于交替优化的算法来处理所考虑的问题，该算法将其分解为两个子问题：通信、感知和计算资源的联合优化，以及UAV轨迹优化。前者通过一种基于连续凸逼近的算法解决，后者则通过一种基于信任区域的算法求解。仿真验证了所提出算法相对于各种基准的有效性，并揭示了LAE系统中通信、感知和计算之间的权衡。
Low-altitude economy (LAE) is an emerging business model, which heavily relies on integrated sensing and communications (ISAC), mobile edge computing (MEC), and covert communications. This paper investigates the convert transmission design in MEC-based networked ISAC systems towards LAE, where an MEC server coordinates multiple access points to simultaneously receive computation tasks from multiple unmanned aerial vehicles (UAVs), locate a target in a sensing area, and maintain UAVs' covert transmission against multiple wardens. We first derive closed-form expressions for the detection error probability (DEP) at wardens. Then, we formulate a total energy consumption minimization problem by optimizing communication, sensing, and computation resources as well as UAV trajectories, subject to the requirements on quality of MEC services, DEP, and radar signal-to-interference-and-noise ratio, and the causality of UAV trajectories. An alternating optimization based algorithm is proposed to handle the considered problem, which decomposes it into two subproblems: joint optimization of communication, sensing, and computation resources, and UAV trajectory optimization. The former is addressed by a successive convex approximation based algorithm, while the latter is solved via a trust-region based algorithm. Simulations validate the effectiveness of the proposed algorithm compared with various benchmarks, and reveal the trade-offs among communication, sensing, and computation in LAE systems.
- [14] arXiv:2507.18323 (交叉列表自 cs.CV) [中文pdf, pdf, html, 其他]
-
标题： 用于ECG描记中半监督语义分割的多数据集基准标题： A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation评论： 6页，2图主题： 计算机视觉与模式识别 (cs.CV) ; 人工智能 (cs.AI) ; 机器学习 (cs.LG) ; 信号处理 (eess.SP)
心电图（ECG）分割，即对有意义的波形特征进行分段，对于临床诊断至关重要。 尽管近年来使用深度学习取得了进展，但由于公开可用的标注数据集稀缺，进展受到限制。 半监督学习通过利用大量未标记的ECG数据提供了一个有前景的解决方案。 在本研究中，我们提出了第一个针对ECG分割的半监督语义分割（SemiSeg）的系统基准。 我们整理并统一了多个公共数据集，包括之前较少使用的来源，以支持稳健且多样的评估。 我们采用了计算机视觉中的五种代表性半监督分割算法，在两种不同的架构上实现：卷积网络和变换器，并在两种不同的设置下进行评估：领域内和跨领域。 此外，我们提出了针对ECG的训练配置和增强策略，并引入了一个标准化的评估框架。 我们的结果表明，在半监督ECG分割中，变换器优于卷积网络。 我们预期我们的基准将作为推进半监督ECG分割方法的基础，并将促进该领域的进一步研究。
Electrocardiogram (ECG) delineation, the segmentation of meaningful waveform features, is critical for clinical diagnosis. Despite recent advances using deep learning, progress has been limited by the scarcity of publicly available annotated datasets. Semi-supervised learning presents a promising solution by leveraging abundant unlabeled ECG data. In this study, we present the first systematic benchmark for semi-supervised semantic segmentation (SemiSeg) in ECG delineation. We curated and unified multiple public datasets, including previously underused sources, to support robust and diverse evaluation. We adopted five representative SemiSeg algorithms from computer vision, implemented them on two different architectures: the convolutional network and the transformer, and evaluated them in two different settings: in-domain and cross-domain. Additionally, we propose ECG-specific training configurations and augmentation strategies and introduce a standardized evaluation framework. Our results show that the transformer outperforms the convolutional network in semi-supervised ECG delineation. We anticipate that our benchmark will serve as a foundation for advancing semi-supervised ECG delineation methods and will facilitate further research in this domain.
交叉提交 (展示 5 之 5 条目 )
- [15] arXiv:2410.13436 (替换) [中文pdf, pdf, html, 其他]
-
标题： 通过图神经网络的多帧检测：一种链接预测方法标题： Multi-frame Detection via Graph Neural Networks: A Link Prediction Approach主题： 信号处理 (eess.SP)
多帧检测算法可以有效利用连续回波之间的相关性，以提高弱目标的检测性能。 现有的高效多帧检测算法通常基于三个顺序步骤：通过相对较低的初始阈值提取图谱，跟踪搜索和跟踪检测。 然而，这些三阶段处理算法可能导致显著的检测性能损失，并未充分利用跨帧的可用回波信息。 在将图神经网络应用于多帧检测时，算法主要基于节点分类任务，无法直接输出目标轨迹。 在本文中，我们将多帧检测问题重新表述为图中的链接预测任务。 首先，我们对超过低阈值的多帧观测进行粗略关联，以构建观测关联图。 随后，设计了一个基于图神经网络的多特征链接预测网络，该网络集成了包括回波结构、多普勒信息以及图谱时空耦合在内的多维信息。 通过利用链接预测原理，我们将跟踪搜索和跟踪检测过程统一为一个步骤，以减少性能损失并直接输出目标轨迹。 实验结果表明，与传统的单帧和多帧检测算法相比，所提出的算法在提高弱目标检测性能的同时抑制了误报。 此外，可解释性分析表明，设计的网络有效地整合了所使用的特征，使得目标与误报之间能够进行准确关联。
Multi-frame detection algorithms can effectively utilize the correlation between consecutive echoes to improve the detection performance of weak targets. Existing efficient multi-frame detection algorithms are typically based on three sequential steps: plot extraction via a relative low primary threshold, track search and track detection. However, these three-stage processing algorithms may result in a notable loss of detection performance and do not fully leverage the available echo information across frames. As to applying graph neural networks in multi-frame detection, the algorithms are primarily based on node classification tasks, which cannot directly output target tracks. In this paper, we reformulate the multi-frame detection problem as a link prediction task in graphs. First, we perform a rough association of multi-frame observations that exceed the low threshold to construct observation association graphs. Subsequently, a multi-feature link prediction network is designed based on graph neural networks, which integrates multi-dimensional information, including echo structure, Doppler information, and spatio-temporal coupling of plots. By leveraging the principle of link prediction, we unifies the processes of track search and track detection into one step to reduce performance loss and directly output target tracks. Experimental results indicate that, compared with traditional single-frame and multi-frame detection algorithms, the proposed algorithm improves the detection performance of weak targets while suppressing false alarms. Additionally, interpretable analysis shows that the designed network effectively integrates the utilized features, allowing for accurate associations between targets and false alarms.
- [16] arXiv:2503.04233 (替换) [中文pdf, pdf, html, 其他]
-
标题： 基于图神经网络的宽带用户调度与混合预编码学习标题： Learning Wideband User Scheduling and Hybrid Precoding with Graph Neural Networks主题： 信号处理 (eess.SP)
用户调度和宽带多天线系统中的混合预编码从未被联合学习过，这是由于在资源块（RBs）上的大量用户组合以及RBs之间的共享模拟预编码所带来的挑战。 在本文中，我们努力使用图神经网络（GNNs）联合学习调度和预编码策略，由于其在跨问题规模泛化方面的潜力，GNNs已成为优化资源分配的强大工具。 通过将联合优化问题重新表述为调度和预编码策略的等效函数优化问题，我们提出了一种由两个级联模块组成的基于GNN的架构来学习这两种策略。 我们发现了一个相同参数相同决策（SPSD）属性，该属性适用于集合上的无线策略，揭示了当用户具有相似信道时，GNN无法很好地学习最优调度策略。 这促使我们开发了一系列GNN来增强调度器模块。 此外，通过分析SPSD属性，我们发现GNN中的线性聚合器会阻碍规模泛化。 基于这一观察，我们在预编码器模块的信息聚合中设计了一种新的注意力机制。 仿真结果表明，所提出的架构在推理时间短、训练复杂度低的情况下实现了满意的频谱效率，并且可以泛化到基站和用户的用户数、RB数和天线数。
User scheduling and hybrid precoding in wideband multi-antenna systems have never been learned jointly due to the challenges arising from the massive user combinations on resource blocks (RBs) and the shared analog precoder among RBs. In this paper, we strive to jointly learn the scheduling and precoding policies with graph neural networks (GNNs), which have emerged as a powerful tool for optimizing resource allocation thanks to their potential in generalizing across problem scales. By reformulating the joint optimization problem into an equivalent functional optimization problem for the scheduling and precoding policies, we propose a GNN-based architecture consisting of two cascaded modules to learn the two policies. We discover a same-parameter same-decision (SPSD) property for wireless policies defined on sets, revealing that a GNN cannot well learn the optimal scheduling policy when users have similar channels. This motivates us to develop a sequence of GNNs to enhance the scheduler module. Furthermore, by analyzing the SPSD property, we find when linear aggregators in GNNs impede size generalization. Based on the observation, we devise a novel attention mechanism for information aggregation in the precoder module. Simulation results demonstrate that the proposed architecture achieves satisfactory spectral efficiency with short inference time and low training complexity, and is generalizable to the numbers of users, RBs, and antennas at the base station and users.
- [17] arXiv:2505.00862 (替换) [中文pdf, pdf, html, 其他]
-
标题： 素数和互质整数矩阵标题： Prime and Co-prime Integer Matrices主题： 信号处理 (eess.SP) ; 离散数学 (cs.DM) ; 信息论 (cs.IT)
本文研究素整数矩阵和互素整数矩阵及其性质。 它表征了所有同时也是素整数矩阵的两两互素整数矩阵。 这提供了一种简单的方法来构造可能在多维互素感知和多维中国剩余定理中有应用的两两互素整数矩阵族。
This paper investigates prime and co-prime integer matrices and their properties. It characterizes all pairwise co-prime integer matrices that are also prime integer matrices. This provides a simple way to construct families of pairwise co-prime integer matrices, that may have applications in multidimensional co-prime sensing and multidimensional Chinese remainder theorem.
- [18] arXiv:2505.07490 (替换) [中文pdf, pdf, html, 其他]
-
标题： 评估基于RRAM的存内计算加速器上二进制和三进制CNN工作负载的可扩展性标题： Evaluating the Scalability of Binary and Ternary CNN Workloads on RRAM-based Compute-in-Memory AcceleratorsJosé Cubero-Cascante, Rebecca Pelke, Noah Flohr, Arunkumar Vaidyanathan, Rainer Leupers, Jan Moritz Joseph评论： 预印本 - 于2025年IEEE计算机协会年度VLSI研讨会（ISVLSI 2025）上发表主题： 信号处理 (eess.SP)
卷积神经网络（CNNs）日益增长的计算需求需要节能的加速策略。基于电阻随机存取存储器（RRAM）的存内计算（CIM）架构通过减少数据移动并实现低功耗的就地计算，提供了一个有前景的解决方案。然而，它们的效率受到外围电路（尤其是模数转换器（ADCs））的高成本限制。通常使用大交叉开关和低ADC分辨率来缓解这一问题，这可能会影响准确性。本工作引入了新的仿真方法，以模拟电阻性线 parasitics 和有限ADC分辨率对RRAM交叉开关的影响。我们的parasitics模型采用矢量化算法，与SPICE相比，交叉开关输出电流的误差低于0.15%。此外，我们提出了一种可变步长ADC和一种校准方法，显著降低了ADC分辨率要求。这些准确性模型与基于统计的能耗模型集成。利用我们的框架，我们对二进制和三进制CNN进行了比较分析。实验结果表明，三进制CNN对线 parasitics 和较低ADC分辨率具有更高的鲁棒性，但能耗效率下降了40%。这些发现为优化用于节能深度学习的RRAM基CIM加速器提供了有价值的见解。
The increasing computational demand of Convolutional Neural Networks (CNNs) necessitates energy-efficient acceleration strategies. Compute-in-Memory (CIM) architectures based on Resistive Random Access Memory (RRAM) offer a promising solution by reducing data movement and enabling low-power in-situ computations. However, their efficiency is limited by the high cost of peripheral circuits, particularly Analog-to-Digital Converters (ADCs). Large crossbars and low ADC resolutions are often used to mitigate this, potentially compromising accuracy. This work introduces novel simulation methods to model the impact of resistive wire parasitics and limited ADC resolution on RRAM crossbars. Our parasitics model employs a vectorised algorithm to compute crossbar output currents with errors below 0.15% compared to SPICE. Additionally, we propose a variable step-size ADC and a calibration methodology that significantly reduces ADC resolution requirements. These accuracy models are integrated with a statistics-based energy model. Using our framework, we conduct a comparative analysis of binary and ternary CNNs. Experimental results demonstrate that the ternary CNNs exhibit greater resilience to wire parasitics and lower ADC resolution but suffer a 40% reduction in energy efficiency. These findings provide valuable insights for optimising RRAM-based CIM accelerators for energy-efficient deep learning.
- [19] arXiv:2506.07685 (替换) [中文pdf, pdf, 其他]
-
标题： 对CommSense测量系统的理论分析标题： Theoretical Analysis for the CommSense Measurement System主题： 信号处理 (eess.SP)
未来6G网络将模糊通信与感知之间的界限，利用无处不在的OFDM波形实现高吞吐量数据和环境感知。 在本工作中，我们对基于通信的感知（CommSense）框架进行了深入分析，该框架将轻量级、基于PCA的检测器嵌入标准OFDM接收器中；实现了实时的、无需设备的被动散射体（例如无人机、车辆等）检测，而无需任何额外发射器。 从一个现实的三链路Rician信道模型（直接Tx到Rx，级联Tx到散射体和散射体到Rx）出发，我们比较了四种检测器：全维似然比检验（Full LRT），基于PCA的LRT，带有线性和RBF核的PCA-SVM。 通过将N维CSI投影到一个P（远小于N）主成分子空间，与全LRT相比，推理时间减少了数量级，同时实现了最优误差率，即经验误差与Bhattacharyya误差边界紧密对齐，ROC曲线下的面积（AUC）近似等于1，当P近似等于10时。 从仿真结果来看，我们展示了基于LRT的技术容易受到参数估计误差的影响，而SVM则对此具有鲁棒性。 我们的结果表明，当与轻量级SVM结合时，基于PCA的检测可以提供快速、准确且稳健的散射体感知，为6G及以后的集成感知与通信（ISAC）铺平道路。
Future 6G networks envisions to blur the line between communication and sensing, leveraging ubiquitous OFDM waveforms for both high throughput data and environmental awareness. In this work, we do a thorough analysis of Communication based Sensing (CommSense) framework that embeds lightweight, PCA based detectors into standard OFDM receivers; enabling real-time, device free detection of passive scatterers (e.g. drones, vehicles etc.) without any extra transmitters. Starting from a realistic three link Rician channel model (direct Tx to Rx, cascaded Tx to Scatterer and Scatterer to Rx), we compare four detectors: the full dimensional Likelihood Ratio Test (Full LRT), PCA based LRT, PCA-SVM with linear and RBF kernels. By projecting N-dimensional CSI onto a P (very less than N) principal component subspace, inference time gets reduced by an order of magnitude compared to the full LRT, while achieving optimal error rates i.e. empirical errors align tightly with the Bhattacharyya error bound and Area Under ROC Curve (AUC) approx. equal to 1 for P approx. equal to 10. From the simulated result we have shown LRT based techniques are susceptible to the parameter estimation error, where as SVM is resilient to that. Our results demonstrate that PCA driven detection when paired with lightweight SVMs can deliver fast, accurate, and robust scatterer sensing, paving the way for integrated sensing and communication (ISAC) in 6G and beyond.
- [20] arXiv:2506.22495 (替换) [中文pdf, pdf, 其他]
-
标题： 掩码自编码器感受心脏：揭示心电图分析中的简单性偏差标题： Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses评论： 存在事实性错误主题： 信号处理 (eess.SP) ; 人工智能 (cs.AI) ; 机器学习 (cs.LG)
心电图（ECG）的诊断价值在于其动态特性，从节律波动到随时间域和频率域演变的细微波形变形。然而，监督式ECG模型往往过度拟合主导性和重复性模式，忽视了细粒度但临床上关键的线索，这种现象称为简单性偏差（SB），其中模型更倾向于学习容易获取的信号而非细微但有信息量的信号。在本工作中，我们首先实证证明了ECG分析中存在SB及其对诊断性能的负面影响，同时发现自监督学习（SSL）可以缓解这一问题，为解决偏差提供了有前景的方向。遵循SSL范式，我们提出了一种新方法，包含两个关键组件：1）时间-频率感知滤波器，用于捕捉反映ECG信号动态特性的时频特征，以及2）在此基础上，构建多粒度原型重建，以在双域中进行粗粒度和细粒度表示学习，进一步减轻SB。为了推进ECG分析中的SSL，我们整理了一个大规模多中心ECG数据集，包含来自300多个临床中心的153万条记录。在六个ECG数据集上的三个下游任务实验表明，我们的方法有效减少了SB并实现了最先进的性能。代码和数据集将公开发布。
The diagnostic value of electrocardiogram (ECG) lies in its dynamic characteristics, ranging from rhythm fluctuations to subtle waveform deformations that evolve across time and frequency domains. However, supervised ECG models tend to overfit dominant and repetitive patterns, overlooking fine-grained but clinically critical cues, a phenomenon known as Simplicity Bias (SB), where models favor easily learnable signals over subtle but informative ones. In this work, we first empirically demonstrate the presence of SB in ECG analyses and its negative impact on diagnostic performance, while simultaneously discovering that self-supervised learning (SSL) can alleviate it, providing a promising direction for tackling the bias. Following the SSL paradigm, we propose a novel method comprising two key components: 1) Temporal-Frequency aware Filters to capture temporal-frequency features reflecting the dynamic characteristics of ECG signals, and 2) building on this, Multi-Grained Prototype Reconstruction for coarse and fine representation learning across dual domains, further mitigating SB. To advance SSL in ECG analyses, we curate a large-scale multi-site ECG dataset with 1.53 million recordings from over 300 clinical centers. Experiments on three downstream tasks across six ECG datasets demonstrate that our method effectively reduces SB and achieves state-of-the-art performance. Code and dataset will be released publicly.
- [21] arXiv:2507.11783 (替换) [中文pdf, pdf, html, 其他]
-
标题： 脑电基础模型：对当前进展和未来方向的批判性综述标题： EEG Foundation Models: A Critical Review of Current Progress and Future Directions评论： 20页，5图，3表（主文+补充）主题： 信号处理 (eess.SP) ; 人工智能 (cs.AI) ; 机器学习 (cs.LG) ; 神经与认知 (q-bio.NC)
通过脑电图（EEG）记录的脑电活动模式在科学和临床研究中具有巨大价值。监督式EEG编码器无法学习稳健的EEG模式，并且过度依赖昂贵的信号标注，这促使人们转向通用的自监督EEG编码器，即EEG基础模型（EEG-FMs），以实现稳健和可扩展的EEG特征提取。然而，早期EEG-FMs的实际应用准备情况以及长期研究进展的标准仍不明确。因此，对第一代EEG-FMs进行系统而全面的综述是必要的，以了解当前最先进的技术水平并确定未来EEG-FMs的关键研究方向。为此，本研究回顾了10个早期的EEG-FMs，并对其方法、实证结果和未解决的研究差距进行了批判性综合分析。我们发现，大多数EEG-FMs采用基于序列的建模方案，该方案依赖于基于Transformer的主干网络，并通过遮蔽序列的重建进行自监督。然而，模型评估仍然存在异质性且主要受限，使得评估其实际现成使用的实用性变得困难。除了采用标准化和现实的评估外，未来的工作应展示更多的扩展效应，并在整个EEG表示学习流程中做出有原则且可信的选择。我们认为，与领域专家合作开发基准、软件工具、技术方法和应用，可能进一步推动EEG-FMs的转化实用性和实际应用。
Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigations. The inability of supervised EEG encoders to learn robust EEG patterns and their over-reliance on expensive signal annotations have sparked a transition towards general-purpose self-supervised EEG encoders, i.e., EEG foundation models (EEG-FMs), for robust and scalable EEG feature extraction. However, the real-world readiness of early EEG-FMs and the rubric for long-term research progress remain unclear. A systematic and comprehensive review of first-generation EEG-FMs is therefore necessary to understand the current state-of-the-art and identify key directions for future EEG-FMs. To that end, this study reviews 10 early EEG-FMs and presents a critical synthesis of their methodology, empirical findings, and outstanding research gaps. We find that most EEG-FMs adopt a sequence-based modeling scheme that relies on transformer-based backbones and the reconstruction of masked sequences for self-supervision. However, model evaluations remain heterogeneous and largely limited, making it challenging to assess their practical off-the-shelf utility. In addition to adopting standardized and realistic evaluations, future work should demonstrate more substantial scaling effects and make principled and trustworthy choices throughout the EEG representation learning pipeline. We believe that developing benchmarks, software tools, technical methodologies, and applications in collaboration with domain experts may further advance the translational utility and real-world adoption of EEG-FMs.
- [22] arXiv:2507.14937 (替换) [中文pdf, pdf, 其他]
-
标题： 相位优化的线性约束最小方差波束成形器标题： Phase-optimised linearly-constrained minimum-variance beamformers评论： 修复了自首次上传以来发现的一些小问题主题： 信号处理 (eess.SP)
一种用于确定线性约束最小方差（LCMV）波束成形器最优群延迟的新方法被提出。推荐了两种选择最优延迟的方法：第一种是使噪声功率最小的解；第二种是使处理延迟最小的解。利用模拟的甚高频（VHF）通信和超高频（UHF）双基地雷达应用，探讨了这一此前未被探索的设计自由度的潜力。
A novel procedure for the determination of the optimal group-delay of a Linearly-Constrained Minimum-Variance (LCMV) beamformer is proposed. Two ways of selecting the optimal delay are recommended: the first is the solution that minimizes the noise power; the second is the solution that minimizes the processing delay. The potential of this hitherto unexplored degree of design freedom is explored using simulated Very-High-Frequency (VHF) communication, and Ultra-High-Frequency (UHF) bistatic radar, applications.
- [23] arXiv:2507.17623 (替换) [中文pdf, pdf, html, 其他]
-
标题： SA-WiSense：一种用于单天线Wi-Fi设备的无盲区呼吸感知框架标题： SA-WiSense: A Blind-Spot-Free Respiration Sensing Framework for Single-Antenna Wi-Fi Devices评论： 12页，10图主题： 信号处理 (eess.SP)
Wi-Fi感知为非接触式人体呼吸监测提供了一种有前景的技术。然而，一个关键挑战是由随机相位偏移引起的盲区问题，这会破坏呼吸信号的互补性。为了解决这一挑战，我们提出了一种单天线Wi-Fi感知（SA-WiSense）框架，以提高人体呼吸监测的准确性，并且对随机相位偏移具有鲁棒性。所提出的SA-WiSense框架成本高效，因为仅使用一个天线，而不是像之前的工作中使用的多个天线。因此，所提出的框架适用于物联网（IoT），其中大多数传感器都配备了一个天线。一方面，我们提出了一种基于交叉子载波信道状态信息（CSI）比值（CSCR）的盲区缓解方法，其中利用子载波之间两个CSI值的比值来减轻随机相位偏移。我们证明了可以通过所提出的CSCR方法取消随机相位偏移，从而恢复信号的固有互补性，实现无盲区感知。另一方面，我们提出了一种基于遗传算法（GA）的子载波选择（GASS）方法，通过将子载波之间的CSCR的感知信噪比（SSNR）作为优化问题进行公式化。GA被用于解决该公式化的优化问题。我们使用商品ESP32微控制器构建了一个实验测试。所提出的方案被验证能够在高达8.0米的距离内实现91.2%的呼吸监测检测率，明显比单天线的最先进方法更准确。
Wi-Fi sensing offers a promising technique for contactless human respiration monitoring. A key challenge, however, is the blind spot problem caused by random phase offsets that corrupt the complementarity of respiratory signals. To address the challenge, we propose a single-antenna-Wi-Fi-sensing (SA-WiSense) framework to improve accuracy of human respiration monitoring, robust against random phase offsets. The proposed SA-WiSense framework is cost-efficient, as only a single antenna is used rather than multiple antennas as in the previous works. Therefore, the proposed framework is applicable to Internet of Thing (IoT), where most of sensors are equipped with a single antenna. On one hand, we propose a cross-subcarrier channel state information (CSI) ratio (CSCR) based blind spot mitigation approach for IoT, where the ratios of two values of CSI between subcarriers are leveraged to mitigate random phase offsets. We prove that the random phase offsets can be cancelled by the proposed CSCR approach, thereby restoring the inherent complementarity of signals for blind-spot-free sensing. On the other hand, we propose a genetic algorithm (GA) based subcarrier selection (GASS) approach by formulating an optimization problem in terms of the sensing-signal-to-noise ratio (SSNR) of CSCR between subcarriers. GA is utilized to solve the formulated optimization problem. We use commodity ESP32 microcontrollers to build an experiment test. The proposed works are validated to achieve an detection rate of 91.2% for respiration monitoring at distances up to 8.0 meters, substantially more accurate than the state-of-the-art methods with a single antenna.
- [24] arXiv:2309.15951 (替换) [中文pdf, pdf, html, 其他]
-
标题： IEEE 802.11be Wi-Fi 7：特性总结和性能评估标题： IEEE 802.11be Wi-Fi 7: Feature Summary and Performance Evaluation主题： 网络与互联网架构 (cs.NI) ; 信号处理 (eess.SP)
随着新兴应用对吞吐量的要求越来越高，IEEE标准802.11be——极高吞吐量（EHT），也称为Wi-Fi 7，于2025年7月22日发布。它可以用于满足4K/8K视频高达几十Gbps的吞吐量需求以及虚拟现实（VR）和增强现实（AR）等低延迟视频应用的需求。Wi-Fi 7不仅将Wi-Fi 6的带宽翻倍，还支持实时应用，这给Wi-Fi带来了革命性的变化。在本文中，我们首先介绍Wi-Fi 7的主要目标和时间表，然后列出最新的关键技术，这些技术促进了Wi-Fi 7性能的提升。最后，我们验证了Wi-Fi 7最重要的目标——潜在的30 Gbps吞吐量和更低的延迟。系统级仿真结果表明，通过结合新技术，Wi-Fi 7实现了比Wi-Fi 6更高的吞吐量和更低的延迟。
As emerging applications demand increasingly higher throughput, IEEE standard 802.11be -- Extremely High Throughput (EHT), also known as Wi-Fi 7, was published on July 22, 2025. It can be used to meet the demand for the throughput of 4K/8K videos up to tens of Gbps and low-latency video applications such as virtual reality (VR) and augmented reality (AR). Wi-Fi 7 not only scales Wi-Fi 6 with doubled bandwidth, but also supports real-time applications, which brings revolutionary changes to Wi-Fi. In this article, we start by introducing the main objectives and timeline of Wi-Fi 7 and then list the latest key techniques which promote the performance improvement of Wi-Fi 7. Finally, we validate the most critical objectives of Wi-Fi 7 -- the potential up to 30 Gbps throughput and lower latency. System-level simulation results suggest that by combining the new techniques, Wi-Fi 7 achieves 30 Gbps throughput and lower latency than Wi-Fi 6.
- [25] arXiv:2410.13812 (替换) [中文pdf, pdf, html, 其他]
-
标题： 私有反事实检索标题： Private Counterfactual Retrieval主题： 信息论 (cs.IT) ; 密码学与安全 (cs.CR) ; 机器学习 (cs.LG) ; 信号处理 (eess.SP)
透明性和可解释性是在高风险应用中使用黑盒机器学习模型时需要考虑的两个极其重要的方面。 提供反事实解释是满足这一要求的一种方式。 然而，这也对提供解释的机构以及请求解释的用户的隐私构成威胁。 在本工作中，我们提出了多种受私有信息检索（PIR）技术启发的方案，以确保在检索反事实解释时的\emph{用户隐私}。 我们提出了一种方案，可以从接受点的数据库中检索出\emph{精确的}最近邻的反事实解释，同时为用户实现完美的（信息论意义上的）隐私。 虽然该方案为用户实现了完美的隐私，但数据库上的某些泄露是不可避免的，我们使用基于互信息的度量来量化这种泄露。 此外，我们提出了减少这种泄露的策略，以实现更高程度的数据库隐私。 我们将这些方案扩展以纳入用户对其属性转换的偏好，从而获得更具操作性的解释。 由于我们的方案依赖于有限域算术，我们在真实数据集上对方案进行了实证验证，以了解准确性和有限域大小之间的权衡。 最后，我们展示了数值结果以支持我们的理论发现，并比较了所提方案的数据库泄露情况。
Transparency and explainability are two extremely important aspects to be considered when employing black-box machine learning models in high-stake applications. Providing counterfactual explanations is one way of fulfilling this requirement. However, this also poses a threat to the privacy of both the institution that is providing the explanation as well as the user who is requesting it. In this work, we propose multiple schemes inspired by private information retrieval (PIR) techniques which ensure the \emph{user's privacy} when retrieving counterfactual explanations. We present a scheme which retrieves the \emph{exact} nearest neighbor counterfactual explanation from a database of accepted points while achieving perfect (information-theoretic) privacy for the user. While the scheme achieves perfect privacy for the user, some leakage on the database is inevitable which we quantify using a mutual information based metric. Furthermore, we propose strategies to reduce this leakage to achieve an advanced degree of database privacy. We extend these schemes to incorporate user's preference on transforming their attributes, so that a more actionable explanation can be received. Since our schemes rely on finite field arithmetic, we empirically validate our schemes on real datasets to understand the trade-off between the accuracy and the finite field sizes. Finally, we present numerical results to support our theoretical findings, and compare the database leakage of the proposed schemes.
- [26] arXiv:2411.06690 (替换) [中文pdf, pdf, html, 其他]
-
标题： 极化感知可移动天线标题： Polarization Aware Movable Antenna主题： 信息论 (cs.IT) ; 信号处理 (eess.SP)
本文提出了一种极化感知可移动天线（PAMA）框架，该框架将极化效应整合到可移动天线（MAs）的设计和优化中。 虽然MAs已被证明能有效提升无线通信性能，但现有研究主要关注由不同传播路径引起的相位变化，并利用天线移动来最大化信道增益。 这种狭窄的关注范围限制了MAs的全部潜力。 在本工作中，我们引入了一个基于电磁理论的极化感知信道模型，揭示了MAs相对于其他无线技术（如预编码）的一个关键优势：优化极化匹配的能力。 这种新理解使PAMA能够将MAs的应用范围从射频、多路径丰富的场景扩展到更高频段，例如毫米波，即使仅存在一条视距（LOS）路径。 我们的研究结果表明，将极化考虑纳入MAs显著提高了效率、链路可靠性和数据吞吐量，为更强大和高效的未来无线网络铺平了道路。
This paper presents a polarization-aware movable antenna (PAMA) framework that integrates polarization effects into the design and optimization of movable antennas (MAs). While MAs have proven effective at boosting wireless communication performance, existing studies primarily focus on phase variations caused by different propagation paths and leverage antenna movements to maximize channel gains. This narrow focus limits the full potential of MAs. In this work, we introduce a polarization-aware channel model rooted in electromagnetic theory, unveiling a defining advantage of MAs over other wireless technologies such as precoding: the ability to optimize polarization matching. This new understanding enables PAMA to extend the applicability of MAs beyond radio-frequency, multipath-rich scenarios to higher-frequency bands, such as mmWave, even with a single line-of-sight (LOS) path. Our findings demonstrate that incorporating polarization considerations into MAs significantly enhances efficiency, link reliability, and data throughput, paving the way for more robust and efficient future wireless networks.
- [27] arXiv:2412.17934 (替换) [中文pdf, pdf, 其他]
-
标题： 无人机通信：障碍物对信道特性的影响标题： UAV Communications: Impact of Obstacles on Channel Characteristics主题： 网络与互联网架构 (cs.NI) ; 信号处理 (eess.SP)
近年来，无人驾驶飞行器（UAVs）已被用作携带Wi-Fi接入点（APs）和蜂窝基站（BSs）的有效平台，从而实现了低成本、灵活且具有高质量服务（QoS）的无线网络。 下一代无线通信将越来越多地依赖于高频段，这些频段容易被障碍物阻挡。 尚未完全解决的关键概念之一是在考虑障碍物的情况下将无人机定位在最佳坐标上。 为了确保无人机与用户设备（UE）之间的视线（LoS），提高QoS，并建立覆盖范围最大的可靠无线链路，必须将障碍物整合到所提出的定位算法中。 本文介绍了一种基于仿真的测量方法，用于在简单场景中表征空对地（AG）信道。 通过考虑障碍物，我们提出了一个关于信道表征的新观点。 在吞吐量、数据包交付、数据包丢失和延迟方面，结果是通过所提出的定位方法进行比较的。
In recent years, Unmanned Aerial Vehicles (UAVs) have been utilized as effective platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations (BSs), enabling low-cost, agile, and flexible wireless networks with high Quality of Service (QoS). The next generation of wireless communications will rely on increasingly higher frequencies, which are easily obstructed by obstacles. One of the most critical concepts yet to be fully addressed is positioning the UAV at optimal coordinates while accounting for obstacles. To ensure a line of sight (LoS) between UAVs and user equipment (UE), improve QoS, and establish reliable wireless links with maximum coverage, obstacles must be integrated into the proposed placement algorithms. This paper introduces a simulation-based measurement approach for characterizing an air-to-ground (AG) channel in a simple scenario. By considering obstacles, we present a novel perspective on channel characterization. The results, in terms of throughput, packet delivery, packet loss, and delay, are compared using the proposed positioning approach.
- [28] arXiv:2501.06726 (替换) [中文pdf, pdf, html, 其他]
-
标题： 集成感知与边缘AI：实现6G中的智能感知标题： Integrated Sensing and Edge AI: Realizing Intelligent Perception in 6G评论： 将出现在IEEE通信调查与教程中主题： 信息论 (cs.IT) ; 信号处理 (eess.SP)
感知和边缘人工智能（AI）被视为第六代（6G）移动网络中两个关键且相互关联的功能。 一方面，感知增强的应用依赖于强大的AI模型，从无处不在的无线传感器中提取特征并理解语义。 另一方面，大量的感官数据作为持续优化边缘AI模型的燃料。 这种感知与边缘AI的深度融合催生了一种新的任务导向范式，称为集成感知和边缘AI（ISEA），其特点是针对通信、AI计算和感知进行整体设计，以实现最佳的感知任务性能。 在本文中，我们对ISEA进行了全面的综述。 我们首先提供ISEA中感知、边缘AI和新通信范式的背景技术。 然后，我们研究ISEA的几个用例，以展示其实际相关性，并介绍当前的标准制定和工业进展。 接下来，确立ISEA的设计原则、度量标准、权衡和架构，随后对ISEA技术进行全面概述，包括数字空中接口、空中计算和先进的信号处理。 其与各种6G进步的相互作用，例如新的物理层和网络技术，也被介绍。 最后，我们提出了ISEA中的未来研究机会，包括基础模型的集成、ISEA与集成感知和通信（ISAC）的融合、超低延迟ISEA以及实用性问题。
Sensing and edge artificial intelligence (AI) are envisioned as two essential and interconnected functions in sixth-generation (6G) mobile networks. On the one hand, sensing-empowered applications rely on powerful AI models to extract features and understand semantics from ubiquitous wireless sensors. On the other hand, the massive amount of sensory data serves as the fuel to continuously refine edge AI models. This deep integration of sensing and edge AI has given rise to a new task-oriented paradigm known as integrated sensing and edge AI (ISEA), which features a holistic design approach to communication, AI computation, and sensing for optimal sensing-task performance. In this article, we present a comprehensive survey for ISEA. We first provide technical preliminaries for sensing, edge AI, and new communication paradigms in ISEA. Then, we study several use cases of ISEA to demonstrate its practical relevance and introduce current standardization and industrial progress. Next, the design principles, metrics, tradeoffs, and architectures of ISEA are established, followed by a thorough overview of ISEA techniques, including digital air interface, over-the-air computation, and advanced signal processing. Its interplay with various 6G advancements, e.g., new physical-layer and networking techniques, are presented. Finally, we present future research opportunities in ISEA, including the integration of foundation models, convergence of ISEA and integrated sensing and communications (ISAC), ultra-low-latency ISEA, and practicality issues.
- [29] arXiv:2502.01108 (替换) [中文pdf, pdf, html, 其他]
-
标题： 脉冲-PPG：一种用于实验室和现场设置中可穿戴应用的开源场训练PPG基础模型标题： Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings评论： 萨哈和徐是共同第一作者主题： 机器学习 (cs.LG) ; 人工智能 (cs.AI) ; 信号处理 (eess.SP)
基于光电容积描记术（PPG）的基础模型由于PPG在生物信号监测中的广泛应用以及其在各种健康应用中具备的泛化潜力而受到关注。 在本文中，我们介绍了Pulse-PPG，这是第一个仅使用在100天实地研究中收集的原始PPG数据训练的开源PPG基础模型，共有120名参与者。 现有的PPG基础模型要么是开源的但使用临床数据进行训练，要么是闭源的，这限制了它们在现实场景中的适用性。 我们在多个数据集和下游任务上评估了Pulse-PPG，并将其性能与一个基于临床数据训练的最先进基础模型进行了比较。 我们的结果表明，Pulse-PPG在未经校正的实地数据上进行训练，在实验室和实地设置中均表现出优于临床和移动健康应用的泛化能力。 这表明，接触现实世界的多样性使模型能够学习到细粒度的表示，使其在不同任务中更具适应性。 此外，在许多任务中，基于实地数据的预训练表现优于基于临床数据的预训练，这加强了在真实世界、多样化数据集上进行训练的重要性。 为了鼓励进一步推进利用实地数据的稳健基础模型，我们计划发布Pulse-PPG，为研究人员提供一个强大的资源，以开发更具有泛化能力的基于PPG的模型。
Photoplethysmography (PPG)-based foundation models are gaining traction due to the widespread use of PPG in biosignal monitoring and their potential to generalize across diverse health applications. In this paper, we introduce Pulse-PPG, the first open-source PPG foundation model trained exclusively on raw PPG data collected over a 100-day field study with 120 participants. Existing PPG foundation models are either open-source but trained on clinical data or closed-source, limiting their applicability in real-world settings. We evaluate Pulse-PPG across multiple datasets and downstream tasks, comparing its performance against a state-of-the-art foundation model trained on clinical data. Our results demonstrate that Pulse-PPG, trained on uncurated field data, exhibits superior generalization across clinical and mobile health applications in both lab and field settings. This suggests that exposure to real-world variability enables the model to learn fine-grained representations, making it more adaptable across tasks. Furthermore, pre-training on field data surprisingly outperforms its pre-training on clinical data in many tasks, reinforcing the importance of training on real-world, diverse datasets. To encourage further advancements in robust foundation models leveraging field data, we plan to release Pulse-PPG, providing researchers with a powerful resource for developing more generalizable PPG-based models.
- [30] arXiv:2506.08418 (替换) [中文pdf, pdf, html, 其他]
-
标题： RadioDUN：一种用于无线电图估计的物理启发深度展开网络标题： RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation主题： 计算机视觉与模式识别 (cs.CV) ; 信号处理 (eess.SP)
无线电图表示区域内频谱资源的空间分布，支持高效的资源分配和干扰缓解。 然而，在实际场景中由于可测量的样本数量有限，构建密集的无线电图非常困难。 虽然现有工作已使用深度学习从稀疏样本中估计密集的无线电图，但它们难以与无线电图的物理特性相结合。 为解决这一挑战，我们将无线电图估计问题转化为稀疏信号恢复问题。 进一步引入一个物理传播模型，将问题分解为多个因子优化子问题，从而降低恢复复杂度。 受现有压缩感知方法的启发，我们提出了无线电深度展开网络（RadioDUN），以展开优化过程，在可学习的方式下实现自适应参数调整和先验拟合。 为了考虑无线电传播特性，我们开发了一个动态重加权模块（DRM），以自适应地建模每个因子对无线电图的重要性。 受物理传播模型中阴影因子的启发，我们集成了与障碍物相关的因素，以表达障碍物引起的信号随机衰减。 进一步设计了阴影损耗来约束因子预测并作为补充监督目标，这增强了RadioDUN的性能。 进行了大量实验以证明所提出的方法优于最先进方法。 我们的代码将在发表后公开提供。
The radio map represents the spatial distribution of spectrum resources within a region, supporting efficient resource allocation and interference mitigation. However, it is difficult to construct a dense radio map as a limited number of samples can be measured in practical scenarios. While existing works have used deep learning to estimate dense radio maps from sparse samples, they are hard to integrate with the physical characteristics of the radio map. To address this challenge, we cast radio map estimation as the sparse signal recovery problem. A physical propagation model is further incorporated to decompose the problem into multiple factor optimization sub-problems, thereby reducing recovery complexity. Inspired by the existing compressive sensing methods, we propose the Radio Deep Unfolding Network (RadioDUN) to unfold the optimization process, achieving adaptive parameter adjusting and prior fitting in a learnable manner. To account for the radio propagation characteristics, we develop a dynamic reweighting module (DRM) to adaptively model the importance of each factor for the radio map. Inspired by the shadowing factor in the physical propagation model, we integrate obstacle-related factors to express the obstacle-induced signal stochastic decay. The shadowing loss is further designed to constrain the factor prediction and act as a supplementary supervised objective, which enhances the performance of RadioDUN. Extensive experiments have been conducted to demonstrate that the proposed method outperforms the state-of-the-art methods. Our code will be made publicly available upon publication.