results: simulation results show that the proposed estimators are superior to state-of-the-art one-bit estimators, and that the more diverse structured sparsity is exploited, the better estimation performance is achieved.Abstract
Recently, intelligent reflecting surface (IRS)-assisted communication has gained considerable attention due to its advantage in extending the coverage and compensating the path loss with low-cost passive metasurface. This paper considers the uplink channel estimation for IRS-aided multiuser massive MISO communications with one-bit ADCs at the base station (BS). The use of one-bit ADC is impelled by the low-cost and power efficient implementation of massive antennas techniques. However, the passiveness of IRS and the lack of signal level information after one-bit quantization make the IRS channel estimation challenging. To tackle this problem, we exploit the structured sparsity of the user-IRS-BS cascaded channels and develop three channel estimators, each of which utilizes the structured sparsity at different levels. Specifically, the first estimator exploits the elementwise sparsity of the cascaded channel and employs the sparse Bayesian learning (SBL) to infer the channel responses via the type-II maximum likelihood (ML) estimation. However, due to the one-bit quantization, the type-II ML in general is intractable. As such, a variational expectation-maximization (EM) algorithm is custom-derived to iteratively compute an ML solution. The second estimator utilizes the common row-structured sparsity induced by the IRS-to-BS channel shared among the users, and develops another type-II ML solution via the block SBL (BSBL) and the variational EM. To further improve the performance of BSBL, a third two-stage estimator is proposed, which can utilize both the common row-structured sparsity and the column-structured sparsity arising from the limited scattering around the users. Simulation results show that the more diverse structured sparsity is exploited, the better estimation performance is achieved, and that the proposed estimators are superior to state-of-the-art one-bit estimators.
摘要
近期，智能反射表面（IRS）助成通信已经吸引了广泛关注，因为它可以延长覆盖范围和补偿路径损失，而且可以通过低成本的pasive metasurface实现。这篇论文考虑了IRS协助多用户大规模MIMO通信的上传通道估计，在BS端使用一bit ADC。由于IRS是pasive的，因此IRS通道估计具有挑战。为解决这个问题，我们利用用户-IRS-BS堆叠通道的结构准确性，并开发了三种通道估计器，每个估计器都利用了不同的结构准确性。第一个估计器利用用户-IRS-BS堆叠通道的元素准确性，使用简单的bayesian学习（SBL）来推断通道响应，并使用类型二最大likelihood（ML）估计。然而，由于一bit quantization，类型二ML通常是不可解的。因此，我们 derivate了一种变量期望-最大化（EM）算法来逐步计算ML解。第二个估计器利用IRS-BS通道中共同的行structured sparsity，开发了另一种类型二ML解决方案，并使用块SBL（BSBL）和变量EM。为了进一步提高BSBL的性能，我们还提出了一种第三种两个阶段估计器，可以利用用户-IRS-BS堆叠通道中的行structured sparsity和列structured sparsity。实验结果表明，随着更多的结构准确性被利用，估计性能得到了提高，并且提出的估计器比state-of-the-art一bit估计器更高效。
Conformal Metamaterials with Active Tunability and Self-adaptivity for Magnetic Resonance Imaging
results: 这个研究获得了一种可以实现灵活调整频率和 selecively 增强磁场的金属结构，这个结构可以实现在诊断中的广泛应用，并且可以解决传统金属结构在诊断中的一些问题。Abstract
Ongoing effort has been devoted to applying metamaterials to boost the imaging performance of magnetic resonance imaging owing to their unique capacity for electromagnetic field confinement and enhancement. However, there are still major obstacles to widespread clinical adoption of conventional metamaterials due to several notable restrictions, namely: their typically bulky and rigid structures, deviations in their optimal resonance frequency, and their inevitable interference with the transmission RF field in MRI. Herein, we address these restrictions and report a conformal, smart metamaterial, which may not only be readily tuned to achieve the desired, precise frequency match with MRI by a controlling circuit, but is also capable of selectively amplifying the magnetic field during the RF reception phase by sensing the excitation signal strength passively, thereby remaining off during the RF transmission phase and thereby ensuring its optimal performance when applied to MRI as an additive technology. By addressing a host of current technological challenges, the metamaterial presented herein paves the way toward the wide-ranging utilization of metamaterials in clinical MRI, thereby translating this promising technology to the MRI bedside.
摘要
Overview of Use Cases in Single Channel Full Duplex Techniques for Satellite Communication
paper_authors: Victor Monzon Baeza, Steven Kisseleff, Jorge Luis González Rios, Juan Andrés Vasquez-Peralvo, Carlos Mosquera, Roberto López Valcarce, Tomás Ramírez Parracho, Pablo Losada Sanisidro, Juan Carlos Merlano Duncan, Symeon Chatzinotas
for: 本研究提供了卫星通信系统中单通道全动态技术的多样化应用和使用场景综述。
methods: 本研究使用了单通道全动态技术，同时在单个频率通道上实现了发送和接收操作。
results: 该研究选择了八个可能的应用场景，并对这些场景进行了初步评估。初步结果表明，单通道全动态技术在各种关键领域内可能带来巨大的改变。Abstract
This paper provides an overview of the diverse range of applications and use cases for Single-Channel Full-Duplex (SCFD) techniques within the field of satellite communication. SCFD, allowing simultaneous transmission and reception on a single frequency channel, presents a transformative approach to enhancing satellite communication systems. We select eight potential use cases with the objective of highlighting the substantial potential of SCFD techniques in revolutionizing SatCom across a multitude of critical domains. In addition, preliminary results from the qualitative assessment are shown. This work is carried out within the European Space Agency (ESA) ongoing activity FDSAT: Single Channel Full Duplex Techniques for Satellite Communications.
摘要
这篇论文提供了卫星通信领域内单频道全动态（SCFD）技术的多样化应用场景和用 caso的概述。 SCFD技术允许在同一个频道上同时进行发送和接收，对卫星通信系统的提升带来了 transformative 的影响。 我们选择了八个可能的应用场景，以强调 SCFD技术在多个关键领域中的巨大潜力。此外，我们还展示了初步的评估结果。这项工作是欧洲空间局（ESA）正在进行的活动FDSAT：单频道全动态技术 для卫星通信。
Meta Reinforcement Learning for Fast Spectrum Sharing in Vehicular Networks
paper_authors: Kai Huang, Le Liang, Shi Jin, Geoffrey Ye Li
for: 这篇论文研究了车辆到所有东西通信中的快速频谱共享问题，以提高整体系统的频谱效率。
methods: 作者使用深度强化学习模型和距离政策优化方法来解决这个问题。
results: 作者的方法可以快速适应新任务，并且可以减少交互次数和训练时间。数据显示，其方法可以达到近似优化性和快速收敛。Abstract
In this paper, we investigate the problem of fast spectrum sharing in vehicle-to-everything communication. In order to improve the spectrum efficiency of the whole system, the spectrum of vehicle-to-infrastructure links is reused by vehicle-to-vehicle links. To this end, we model it as a problem of deep reinforcement learning and tackle it with proximal policy optimization. A considerable number of interactions are often required for training an agent with good performance, so simulation-based training is commonly used in communication networks. Nevertheless, severe performance degradation may occur when the agent is directly deployed in the real world, even though it can perform well on the simulator, due to the reality gap between the simulation and the real environments. To address this issue, we make preliminary efforts by proposing an algorithm based on meta reinforcement learning. This algorithm enables the agent to rapidly adapt to a new task with the knowledge extracted from similar tasks, leading to fewer interactions and less training time. Numerical results show that our method achieves near-optimal performance and exhibits rapid convergence.
摘要
在本文中，我们研究了车辆到所有东西通信中快速spectrum sharing的问题。为了提高整个系统的spectrum效率，车辆到基础设施链路的spectrum被重复利用于车辆到车辆链路。为此，我们将其模型为深度优化学习问题，使用距离策略优化进行解决。由于通信网络中的训练经常需要大量的交互，因此通常使用模拟基础进行训练。然而，在实际环境中直接部署Agent可能会导致性能下降，即使Agent在模拟器上表现良好，因为实际环境和模拟器之间存在差距。为Addressing这个问题，我们提出了基于meta学习算法的方法。这种算法使得Agent能够快速适应新任务，通过提取相似任务中的知识，从而减少交互次数和训练时间。数字实验结果表明，我们的方法可以达到近似优化性和快速收敛。
D-Band 2D MIMO FMCW Radar System Design for Indoor Wireless Sensing
results: 研究发现，随着目标距离的增加，使用64个天线的MUSIC算法可以在1-10米室内距离和0-30dB SNR范围内提供较低的root-mean-square error（RMSE），而使用16个天线和4个天线的情况下，两种算法具有相似的性能。此外，研究还探讨了雷达接收器（RX）信号强度和发送器（TX）输出功率的关系，并对现有D频段半导体雷达的输出功率要求进行了比较。Abstract
In this article, we present system design of D-band multi-input multi-output (MIMO) frequency-modulated continuous-wave (FMCW) radar for indoor wireless sensing. A uniform rectangular array (URA) of radar elements is used for 2D direction-of-arrival (DOA) estimation. The DOA estimation accuracy of the MIMO radar array in the presence of noise is evaluated using the multiple-signal classification (MUSIC) and the minimum variance distortionless response (MVDR) algorithms. We investigate different scaling scenarios for the radar receiver (RX) SNR and the transmitter (TX) output power with the target distance. The DOA estimation algorithm providing the highest accuracy and shortest simulation time is shown to depend on the size of the radar array. Specifically, for a 64-element array, the MUSIC achieves lower root-mean-square error (RMSE) compared to the MVDR across 1--10\,m indoor distances and 0--30\,dB SNR (e.g., $\rm 0.8^{\circ}$/$\rm 0.3^{\circ}$ versus $\rm 1.0^{\circ}$/$\rm 0.5^{\circ}$ at 10/20\,dB SNR and 5\,m distance) and 0.5x simulation time. For a 16-element array, the two algorithms provide comparable performance, while for a 4-element array, the MVDR outperforms the MUSIC by a large margin (e.g., $\rm 8.3^{\circ}$/$\rm 3.8^{\circ}$ versus $\rm 62.2^{\circ}$/$\rm 48.8^{\circ}$ at 10/20\,dB SNR and 5\,m distance) and 0.8x simulation time. Furthermore, the TX output power requirement of the radar array is investigated in free-space and through-wall wireless sensing scenarios, and is benchmarked by the state-of-the-art D-band on-chip radars.
摘要
在这篇文章中，我们介绍了D频段多入口多出口（MIMO）频率变化 kontinuierlich（FMCW）雷达的系统设计，用于室内无线感知。我们使用了一个均匀的方正阵（URA）来实现2D方向到来（DOA）估算。我们使用MUSIC和MVDR算法来评估FMCW雷达数组在噪声存在下的DOA估算精度。我们 investigate了不同的收发器信号强度（RX SNR）和发射器输出功率（TX output power）的涨落情况，并对目标距离进行了 investigate。我们发现，对于64个元素数组，MUSIC算法在1-10米室内距离和0-30dB SNR范围内提供了更低的平均方差误差（RMSE），比如0.8度/0.3度相比于1.0度/0.5度，而且 simulation time 相对较短。对于16个元素数组，两种算法具有相似的性能，而对于4个元素数组，MVDR算法明显超过MUSIC算法，例如，在10/20dB SNR和5米距离下，MVDR算法的误差为8.3度/3.8度，而MUSIC算法的误差为62.2度/48.8度，而且 simulation time 相对较长。此外，我们还调查了雷达数组的发射器输出功率要求，并与现有的D频段在芯片上的雷达相比。
White Paper on Radio Channel Modeling and Prediction to Support Future Environment-aware Wireless Communication Systems
paper_authors: Mate Boban, Vittorio Degli-Esposti for:The paper is written to provide an overview of the state-of-the-art in radio channel measurement and modeling, and to identify the key challenges that need to be addressed to support the development of 6G networks.methods:The paper uses a variety of methods, including channel sounder design, metrology, and measurement methodologies, as well as measurements, modeling, and systematic dataset collection and analysis.results:The paper provides a summary of the state-of-the-art in radio channel measurement and modeling, and identifies the key challenges that the scientific community will need to address to support the development of 6G networks. These challenges include the need for a paradigm shift in channel measurements and modeling, the need for a wider frequency range, and the need for better support for diverse and highly cluttered environments.Here is the information in Simplified Chinese text:for:本文目的是提供 radio 频道测量和模型的状况报告，以及为支持未来的 6G 网络发展而需要解决的主要挑战。methods:本文使用了多种方法，包括通道测量设计、测量方法学、测量方法和系统atic dataset收集和分析。results:本文提供了 radio 频道测量和模型的状况报告，并确定了支持未来的 6G 网络发展所需的主要挑战。这些挑战包括需要一种新的测量和模型 парадиг shift，需要更广泛的频谱范围，以及更好地支持多样化和高度干扰的环境。Abstract
COST INTERACT working group (WG)1 aims at increasing the theoretical and experimental understanding of radio propagation and channels in environments of interest and at deriving models for design, simulation, planning and operation of future wireless systems. Wide frequency ranges from sub-GHz to terahertz (THz), potentially high mobility, diverse and highly cluttered environments, dense networks, massive antenna systems, and the use of intelligent surfaces, are some of the challenges for radio channel measurements and modeling for next generation systems. As indicated in [1], with increased number of use cases (e.g., those identified by one6G [2] and shown in Fig. 1) to be supported and a larger number of frequency bands, a paradigm shift in channel measurements and modeling will be required. To address the particular challenges that come with such a paradigm shift, WG1 started the work on relevant topics, ranging from channel sounder design, metrology and measurement methodologies, measurements, modeling, and systematic dataset collection and analysis. In addition to the core activities of WG1, based on the strong interest of the participants, two sub-working groups (subWGs) have been initiated as part of WG1: i) subWG1.1 on millimeter-wave (mmWave) and THz sounding (subWG THz) and ii) subWG1.2 on propagation aspects related to reconfigurable intelligent surfaces (RIS) (subWG RIS). This white paper has two main goals: i) it summarizes the state-of-theart in radio channel measurement and modeling and the key challenges that the scientific community will have to face over the next years to support the development of 6G networks, as identified by WG1 and its subWGs; and ii) it charts the main directions for the work of WG1 and subWGs for the remainder of COST INTERACT duration (i.e., until October 2025).
摘要
Double-Layer Power Control for Mobile Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning
results: 对于不同的MARL算法进行比较，研究发现，提议的MARL算法能够均衡 spectral efficiency（SE）性能和整合时间。此外，对于双层电力控制架构，结果表明，相比单层架构，提议的双层架构在巨大天线数和小天线间距下具有24%的SE性能提升。Abstract
Cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as a promising technology for enabling future wireless communication systems. Significant attention has been generated by its considerable advantages in augmenting degrees of freedom. In this paper, we first investigate a CF XL-MIMO system with base stations equipped with XL-MIMO panels under a dynamic environment. Then, we propose an innovative multi-agent reinforcement learning (MARL)-based power control algorithm that incorporates predictive management and distributed optimization architecture, which provides a dynamic strategy for addressing high-dimension signal processing problems. Specifically, we compare various MARL-based algorithms, which shows that the proposed MARL-based algorithm effectively strikes a balance between spectral efficiency (SE) performance and convergence time. Moreover, we consider a double-layer power control architecture based on the large-scale fading coefficients between antennas to suppress interference within dynamic systems. Compared to the single-layer architecture, the results obtained unveil that the proposed double-layer architecture has a nearly24% SE performance improvement, especially with massive antennas and smaller antenna spacing.
摘要
cell-free（CF） extremly large-scale multiple-input multiple-output（XL-MIMO）被视为未来无线通信系统的促进技术。这种技术具有显著的优势，可以增加度量自由。在这篇论文中，我们首先investigated CF XL-MIMO系统，其中基站装备了XL-MIMO板。然后，我们提出了一种基于多智能学习（MARL）的动态管理和分布式优化架构的新型电力控制算法。这种算法可以在高维度信号处理问题上提供一个动态策略，并且能够均衡spectral efficiency（SE）性能和整合时间。此外，我们还考虑了一种双层电力控制架构，基于大规模投射系数 между天线来降低动态系统中的干扰。与单层架构相比，我们发现，提posed double-layer架构在巨量天线和小天线间隔下具有24%的SE性能提升。
Energy-Efficient Secure Offloading System Designed via UAV-Mounted Intelligent Reflecting Surface for Resilience Enhancement
results: 该论文通过优化地面用户设备的发射功率、UAV-mounted IRS的轨迹和相位偏移矩阵，以及卸载比率 между本地执行和边缘计算，使用successive convex approximation（SCA）算法，可以提供较大的能源储存量相比于本地执行和部分优化。Abstract
With increasing interest in mmWave and THz communication systems, an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) has been suggested as a key enabling technology to establish robust line-of-sight (LoS) connections with ground nodes owing to their free mobility and high altitude, especially for emergency and disaster response. This paper investigates a secure offloading system, where the UAV-mounted IRS assists the offloading procedures between ground users and an access point (AP) acting as an edge cloud. In this system, the users except the intended recipients in the offloading process are considered as potential eavesdroppers. The system aims to achieve the minimum total energy consumption of battery-limited ground user devices under constraints for secure offloading accomplishment and operability of UAV-mounted IRS, which is done by optimizing the transmit power of ground user devices, the trajectory and phase shift matrix of UAV-mounted IRS, and the offloading ratio between local execution and edge computing based on the successive convex approximation (SCA) algorithms. Numerical results show that the proposed algorithm can provide the considerable energy savings compared with local execution and partial optimizations.
摘要
随着mmWave和THz通信系统的兴趣增长，一种随机飞行器（UAV）搭载智能反射表（IRS）已被认为是建立可靠直线视线（LoS）连接地面节点的关键技术，尤其是在紧急和灾难应急情况下。这篇论文研究了一种安全卸载系统，其中UAV搭载IRS协助地面用户和edge云（AP）之间的卸载过程。在这个系统中，地面用户除了指定接收者外的其他用户都被视为潜在的窃听者。系统的目标是在续ouseless执行和UAV搭载IRS的可操作性下，最小化电池限制的地面用户设备的总能量占用，通过优化地面用户设备的传输功率、UAV搭载IRS的轨迹和相位偏移矩阵，以及在本地执行和edge计算之间的卸载比例，使用successive convex approximation（SCA）算法来实现。numerical results表明，提议的算法可以提供较大的能量减少 compared with本地执行和部分优化。
Tree based Single LED Indoor Visible Light Positioning Technique
results: 这个技术可以获得非常高精度的三维定位结果，其中三维定位误差为2.88厘米，比 closest competitor 的6.26厘米更低。Abstract
Visible light positioning(VLP) has gained prominence as a highly accurate indoor positioning technique. Few techniques consider the practical limitations of implementing VLP systems for indoor positioning. These limitations range from having a single LED in the field of view(FoV) of the image sensor to not having enough images for training deep learning techniques. Practical implementation of indoor positioning techniques needs to leverage the ubiquity of smartphones, which is the case with VLP using complementary metal oxide semiconductor(CMOS) sensors. Images for VLP can be gathered only after the lights in question have been installed making it a cumbersome process. These limitations are addressed in the proposed technique, which uses simulated data of a single LED to train machine learning models and test them on actual images captured from a similar experimental setup. Such testing produced mean three dimensional(3D) positioning error of 2.88 centimeters while training with real images achieves accuracy of less than one centimeter compared to 6.26 centimeters of the closest competitor.
摘要
可见光定位(VLP) 已经成为室内定位技术中的一种非常精度的方法。然而，只有几种技术考虑到实际实施 VLP 系统的实际限制。这些限制包括在图像传感器的视场中只有一个 LED 存在以及没有足够的图像用于深度学习技术的训练。实际应用室内定位技术应该利用智能手机的普遍性，这是 VLP 使用 complementary metal oxide semiconductor (CMOS) 传感器的情况。图像 для VLP 只能在灯光问题上安装后获得，这是一个繁琐的过程。这些限制被提出的方法解决，该方法使用单个 LED 的 simulate 数据来训练机器学习模型，然后在实际设置上测试这些模型。这种测试产生的三维定位误差为 2.88 厘米，而使用实际图像训练的精度则为 Less than 1 厘米，与最近竞争对手的 6.26 厘米误差相比。