For: The paper is written for researchers and scientists who use NMR spectroscopy to study biological macromolecules, specifically those who use multidimensional NMR spectroscopy and non-uniform sampling (NUS) techniques.* Methods: The paper proposes a new sampling schedule called SCPG (Symmetrical Copy Poisson Gap) and uses compressed sensing (CS) methods for reconstruction. The authors theoretically prove that the symmetrical constraint in SCPG is equivalent to sparsity, which improves the accuracy of NUS reconstruction.* Results: The authors show that the proposed SCPG sampling schedule outperforms state-of-the-art 2D Woven PG in NUS reconstruction for symmetrical NMR spectroscopy, both in simulated and experimental data.Here are the three points in Simplified Chinese text:
results: 作者表明，SCPG 抽样时间表在对 symmetrical NMR спектроскопия的 NUS 重建中比 state-of-the-art 2D Woven PG 高效， both in 模拟和实验数据中。Abstract
Symmetrical NMR spectroscopy constitutes a vital branch of multidimensional NMR spectroscopy, providing a powerful tool for the structural elucidation of biological macromolecules. Non-Uniform Sampling (NUS) serves as an effective strategy for averting the prohibitive acquisition time of multidimensional NMR spectroscopy by only sampling a few points according to NUS sampling schedules and reconstructing missing points via algorithms. However, current sampling schedules are unable to maintain the accurate recovery of cross peaks that are weak but important. In this work, we propose a novel sampling schedule termed as SCPG (Symmetrical Copy Poisson Gap) and employ CS (Compressed Sensing) methods for reconstruction. We theoretically prove that the symmetrical constraint, apart from sparsity, is implicitly implemented when SCPG is combined with CS methods. The simulated and experimental data substantiate the advantage of SCPG over state-of-the-art 2D Woven PG in the NUS reconstruction of symmetrical NMR spectroscopy.
摘要
同对称NMR光谱学是生物大分子结构解析的重要分支，具有强大的工具。非均匀抽样（NUS）是一种有效的策略，以减少多维度NMR光谱学的质量点扩展时间。然而，目前的抽样计划无法确保强度较弱但重要的交叉峰织入的精确重建。在这个工作中，我们提出一个新的抽样计划，称为SCPG（对称复复点差隔），并使用CS（压缩感知）方法进行重建。我们 teorically证明，在SCPG与CS方法的结合下，还会隐式地实现对称限制。实验和资料 validate SCPG的优势，比顶部的2D维织PG在NUS重建中。
6G Positioning and Sensing Through the Lens of Sustainability, Inclusiveness, and Trustworthiness
paper_authors: Henk Wymeersch, Hui Chen, Hao Guo, Musa Furkan Keskin, Bahare M. Khorsandi, Mohammad H. Moghaddam, Alejandro Ramirez, Kim Schindhelm, Athanasios Stavridis, Tommy Svensson, Vijaya Yajnanarayana
results: 本研究发现，6G技术可以通过增强位置和感知的集成来提高通信性能，同时也可以实现包容性和可信worthiness的价值观念。然而，这些价值观念与传统的性能指标之间存在融合关系，需要在设计和实现6G技术时进行综合考虑。Abstract
6G promises a paradigm shift in which positioning and sensing are inherently integrated, enhancing not only the communication performance but also enabling location- and context-aware services. Historically, positioning and sensing have been viewed through the lens of cost and performance trade-offs, implying an escalated demand for resources, such as radio, physical, and computational resources, for improved performance. However, 6G goes beyond this traditional perspective to encompass a set of broader values, namely sustainability, inclusiveness, and trustworthiness. This paper aims to: (i) shed light on these important value indicators and their relationship with the conventional key performance indicators, and (ii) unveil the dual nature of 6G in relation to these key value indicators (i.e., ensuring operation according to the values and enabling services that affect the values).
摘要
6G 承诺一种 Paradigm shift， Positioning 和 Sensing 被内置地集成，不仅提高了通信性能，还启用了 Location-和 Context-aware 服务。历史上，Positioning 和 Sensing 通常被视为成本和性能之间的贸易OFF，这意味着需要更多的 radio、物理和计算资源来提高性能。然而，6G 超越了传统的视角，涵盖更广泛的价值观念，包括可持续性、包容性和信任性。本文的目标是：（i）探讨这些重要的价值指标与传统的关键性能指标之间的关系，（ii）揭示 6G 对这些价值指标的双重性质（即，根据价值来运行并提供影响价值的服务）。
Identification of Ghost Targets for Automotive Radar in the Presence of Multipath
paper_authors: Le Zheng, Jiamin Long, Marco Lops, Fan Liu, Xueyao Hu for:The paper is written for detecting the presence of ghosts in automotive radar systems due to multipath.methods:The paper uses a composite hypothesis testing approach based on the Generalized Likelihood Ratio Test (GLRT) philosophy, combined with a sparsity-enforced Compressed Sensing (CS) approach and Levenberg-Marquardt (LM) optimization to estimate the angular parameters in the continuous domain.results:The paper provides an extensive performance analysis to validate the proposed solution for detecting ghosts in automotive radar systems.Abstract
Colocated multiple-input multiple-output (MIMO) technology has been widely used in automotive radars as it provides accurate angular estimation of the objects with relatively small number of transmitting and receiving antennas. Since the Direction Of Departure (DOD) and the Direction Of Arrival (DOA) of line-of-sight targets coincide, MIMO signal processing allows forming a larger virtual array for angle finding. However, multiple paths impinging the receiver is a major limiting factor, in that radar signals may bounce off obstacles, creating echoes for which the DOD does not equal the DOA. Thus, in complex scenarios with multiple scatterers, the direct paths of the intended targets may be corrupted by indirect paths from other objects, which leads to inaccurate angle estimation or ghost targets. In this paper, we focus on detecting the presence of ghosts due to multipath by regarding it as the problem of deciding between a composite hypothesis, ${\cal H}_0$ say, that the observations only contain an unknown number of direct paths sharing the same (unknown) DOD's and DOA's, and a composite alternative, ${\cal H}_1$ say, that the observations also contain an unknown number of indirect paths, for which DOD's and DOA's do not coincide. We exploit the Generalized Likelihood Ratio Test (GLRT) philosophy to determine the detector structure, wherein the unknown parameters are replaced by carefully designed estimators. The angles of both the active direct paths and of the multi-paths are indeed estimated through a sparsity-enforced Compressed Sensing (CS) approach with Levenberg-Marquardt (LM) optimization to estimate the angular parameters in the continuous domain. An extensive performance analysis is finally offered in order to validate the proposed solution.
摘要
协同多输入多出口（MIMO）技术在汽车雷达中广泛应用，因为它可以准确地估算目标物的方向，只需使用相对较少的发射和接收天线。由于发射和接收方向的DOD和DOA相同，MIMO信号处理可以组成较大的虚拟数组，以便角度测量。但是，多路射雷达信号可以受到障碍物的反射，导致信号返回不同的方向，从而导致DOD不等于DOA。因此，在复杂的多散体场景下，直接目标的直接路径可能会受到其他 объек的 indirect 路径的扰动，从而导致角度估算不准确或鬼目标。在这篇论文中，我们关注在多射场景中 Ghost 的探测，即在雷达信号中检测到不同的 DOD 和 DOA 的射频信号是否来自于直接或间接的多射。我们采用 Generalized Likelihood Ratio Test（GLRT）哲学来确定探测结构，其中未知参数被换成精心设计的估计器。雷达信号中的直接路径和多射路径的角度都是通过一种减少维度的 Compressed Sensing（CS）方法和 Levenberg-Marquardt（LM）优化来估计的。 finally，我们提供了广泛的性能分析，以验证我们的提案的可行性。
Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO
results: 理论结果表明，提案的方案可以减少射频过头量化频率域和时域频率域的频率稀热性，以实现低射频过头的多输入多出力通道估计，同时保证估计精度。实验结果表明，对于5G FDD巨量MIMO系统，提案的方案可以减少射频过头量化80%以上，而且估计精度与传统CS方案相当。Abstract
Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas require large volumes of pilot overhead to guarantee reliable channel estimation in FDD massive MIMO system. Compressive sensing (CS) has been applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel but suffer from high complexity. To overcome this challenge, this paper develops a hybrid channel estimation scheme by integrating the model-driven CS and data-driven deep unrolling technique. The proposed scheme consists of a coarse estimation part and a fine correction part to respectively exploit the inter- and intraframe sparsities of channels to greatly reduce the pilot overhead. Theoretical result is provided to indicate the convergence of the fine correction and coarse estimation net. Simulation results are provided to verify that our scheme can estimate MIMO channels with low pilot overhead while guaranteeing estimation accuracy with relatively low complexity.
摘要
大量多输入多输出（MIMO）在5G无线通信系统中具有优异的优势，主要是在频率和能量方面。然而，数百个天线需要大量的射频过头来保证可靠的通道估计在FDD大量MIMO系统中。压缩感知（CS）已经应用于通道估计中，利用大量MIMO通道的自然稀畴结构。然而，它受到高复杂性的挑战。为了解决这个挑战，本文提出了一种混合模型驱动CS和数据驱动深层卷积技术的混合通道估计方案。该方案包括粗略估计部分和精度修正部分，分别利用通道之间和通道内部的稀畴性来大幅减少射频过头。我们提供了理论结果，证明了精度修正和粗略估计网的共振。实验结果表明，我们的方案可以在低射频过头下Estimation MIMO通道的精度，而且与相对较低的复杂性。