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Fluid Antenna Systems Enabling 6G:Principles, Applications, and Research Directions

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arXiv 2024年

作者： Wu, Tuo Zhi, Kangda Yao, Junteng Lai, Xiazhi Zheng, Jianchao Niu, Hong Elkashlan, Maged Wong, Kai-Kit Chae, Chan-Byoung Ding, Zhiguo Karagiannidis, George K. Debbah, Mérouane Yuen, Chau The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore The School of Electrical Engineering and Computer Science Technical University of Berlin Berlin10623 Germany The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China The School of Computer Science Guangdong University of Education Guangdong Guangzhou China The School of Computer Science and Engineering Huizhou University Huizhou516000 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Yonsei Frontier Laboratory Yonsei University Seoul03722 Korea Republic of The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of The Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates The Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Lebanon Lebanon The Center for 6G Technology Khalifa University of Science and Technology P. O. Box 127788 Abu Dhabi United Arab Emirates

Fluid antenna system (FAS), a novel advancement in reconfigurable antenna technologies, offers unprecedented shape and position flexibility. This innovative approach is emerging as an exciting and potentially transformative technology for wireless communication systems. FAS encompasses any software-controlled fluidic, conductive, or dielectric structure capable of dynamically altering an antenna’s shape and position to modify critical parameters such as gain, radiation pattern, operating frequency, and other key characteristics. Given its unique capabilities, FAS is highly anticipated to play a significant role in shaping the upcoming sixth-generation (6G) wireless networks. This article explores this potential by addressing four major questions: 1) Is FAS crucial to 6G? 2) How can FAS be characterized? 3) What are the applications of FAS? 4) What challenges and future research directions are associated with FAS? In particular, five promising research directions that highlight FAS’s potential are examined. Finally, we conclude by demonstrating the impressive performance of FAS, reinforcing its relevance and promise in future networks. © 2024, CC BY.

关键词： Liquefied gases

Virtual metasurfaces:reshaping electromagnetic waves in distance

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Photonics Research 2023年第2期11卷 203-211页

作者： RUICHAO ZHU JIAFU WANG YAJUAN HAN YUXIANG JIA TONGHAO LIU TIANSHUO QIU SAI SUI YONGFENG LI MINGBAO YAN SHAOBO QU CHENG-WEI QIU Shaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices Air Force Engineering UniversityXi’an 710051China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore NUS Suzhou Research Institute(NUSRI) Suzhou 215000China

Metasurface has provided unprecedented freedoms in manipulating electromagnetic(EM) waves, exhibiting fascinating functions. Conventionally, these functions are implemented right on metasurfaces, where spatial modulations on EM wave amplitudes or phases are achieved by meta-atoms. This study proposes the concept of virtual metasurface(VM), which is formed by arrays of foci away from the entity metasurface. Unlike conventional metasurfaces, spatial modulations on the amplitudes or phases of EM waves occur in the air, with a focal length distance from the entity metasurface. As a proof of concept, we demonstrated a transmissive VM. The entity metasurface consists of transmissive focusing metasurface tiles(TFMTs) with the same focal length. Two TFMTs were designed with phase difference π to enable the most typical checkerboard configuration. The TFMTs were assembled to form the entity metasurface, whereas their foci formed the VM. Due to the π phase difference among adjacent foci, EM propagation along the normal direction was cancelled, leading to four tilted far-field beams. The concept of VM can be readily extended to higher frequencies from terahertz to optical regimes and may find wide applications in communication, camouflage, and other fields.

关键词： surface. surfaces proof

On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach

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arXiv 2025年

作者： Liu, Yuhan Kiss, Máté Tóth, Roland Schoukens, Maarten Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest1111 Hungary

While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems, where robustness to extrapolation/interpolation errors is prioritized over minimizing estimated parameter variance. To address this issue, we develop a novel identification-oriented space-filling input design strategy for efficient covering of a well-defined region of interest. By placing a Gaussian Process (GP) prior on the joint input-state space, the proposed strategy leverages the GP posterior variance to construct a space-filling promoting input design cost function from the Bayesian point of view. This accordingly enables maximization of the coverage in the region of interest, thereby facilitating the generation of informative datasets. Furthermore, we theoretically prove that minimization of the cost function indicates the space-filling behavior. The effectiveness of the proposed strategy is demonstrated on both an academic example and a mass-spring-damper system, highlighting its potential practical impact on efficient exploration in identification scenarios for nonlinear dynamical systems. © 2025, CC BY-NC-SA.

关键词： Cost functions

On Improved Commutation for Moving-Magnet Planar Actuators

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arXiv 2023年

作者： Broens, Yorick Butler, Hans Tóth, Roland The Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Hungary

The demand for high-precision and high-throughput motion control systems has increased significantly in recent years. The use of moving-magnet planar actuators (MMPAs) is gaining popularity due to their advantageous characteristics, such as complete environmental decoupling and reduction of stage mass. Nonetheless, model-based commutation techniques for MMPAs are compromised by misalignment between the mover and coil array and mismatch between the ideal electromagnetic model and the physical system, often leading to decreased system performance. To address this issue, a novel improved commutation approach is proposed in this paper, which is applicable for general planar motor applications, by means of dynamic regulation of the position dependence of the ideal model-based commutation algorithm, which allows for attenuation of magnetic misalignment, manufacturing inaccuracies and other unmodelled phenomena. The effectiveness of the proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator *** Codes 37M15 © 2023, CC BY.

关键词： Alignment

Holistic Network Virtualization and Pervasive Network Intelligence for 6G

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arXiv 2023年

作者： Shen, Xuemin Gao, Jie Wu, Wen Li, Mushu Zhou, Conghao Zhuang, Weihua The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Computer Engineering Marquette University MilwaukeeWI53233 United States The Frontier Research Center Peng Cheng Laboratory Guangdong Shenzhen518055 China

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G. © 2023, CC BY-NC-SA.

关键词： Network architecture

Variational Bayesian Learning based Joint Localization and Path Loss Exponent with Distance-dependent Noise in Wireless Sensor Network

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arXiv 2024年

作者： Li, Yunfei Luo, Yiting Tan, Weiqiang Li, Chunguo Ma, Shaodan Yang, Guanghua The Department of Electrical Engineering Anhui Polytechnic University Wuhu China The School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China The School of Information Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau Taipa China The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

This paper focuses on the challenge of jointly optimizing location and path loss exponent (PLE) in distance-dependent noise. Departing from the conventional independent noise model used in localization and path loss exponent estimation problems, we consider a more realistic model incorporating distance-dependent noise variance, as revealed in recent theoretical analyses and experimental results. The distance-dependent noise introduces a complex noise model with unknown noise power and PLE, resulting in an exceptionally challenging non-convex and nonlinear optimization problem. In this study, we address a joint localization and path loss exponent estimation problem encompassing distance-dependent noise, unknown parameters, and uncertainties in sensor node locations. To surmount the intractable nonlinear and non-convex objective function inherent in the problem, we introduce a variational Bayesian learning-based framework that enables the joint optimization of localization, path loss exponent, and reference noise parameters by leveraging an effective approximation to the true posterior distribution. Furthermore, the proposed joint learning algorithm provides an iterative closed-form solution and exhibits superior performance in terms of computational complexity compared to existing algorithms. computer simulation results demonstrate that the proposed algorithm approaches the performance of the Bayesian Cramer-Rao bound (BCRB), achieves localization performance comparable to the (maximum likelihood-Gaussian message passing) ML-GMP algorithm in some cases, and outperforms the other comparison algorithm in all cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensor nodes

Near-field radiative heat transfer between nonpolar epsilon-near-zero dielectric-filled Si gratings

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Physical Review B 2024年第12期109卷 125430-125430页

作者： Gui-Cheng Cui Cheng-Long Zhou Yong Zhang Hong-Liang Yi School of Energy Science and Engineering Harbin Institute of Technology Harbin 150001 People's Republic of China Key Laboratory of Aerospace Thermophysics Ministry of Industry and Information Technology Harbin 150001 People's Republic of China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore

Epsilon-near-zero (ENZ) materials have attracted significant attention in the far- and near fields of thermal radiation in recent years because of their unique optical characteristics. However, it is not considered an optimal carrier for near-field radiative heat transfer (NFRHT) due to the excessively low frustrated mode. In this paper, we address this drawback with a metamaterial composed of artificial hypothetical nonpolar ENZ dielectric-filled Si gratings. The behavior of NFRHT with ENZ has been investigated based on fluctuational electrodynamic and rigorous coupled-wave analysis. An artificial mode named Meta-NP ENZ mode is presented to reveal the significant enhancement of NFRHT, which can be demonstrated by the electric field intensity enhancement. Furthermore, we find that the increasing imaginary part may lead to an anomalous amplification of heat flux despite causing a recession in the Meta-NP ENZ mode, and this mode remains robust with respect to the plasma frequency shift of grating material. Our findings demonstrate that the ENZ dielectric exhibits outstanding performances similar to those observed in far-field radiation, surpassing the limitations of both Si gratings and nonpolar ENZ dielectric in NFRHT.

关键词： Permittivity Surface states Dielectrics Drude model Rigorous coupled-wave analysis

Development of Surface Rain Estimates from Tempest-D Observations

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Development of Surface Rain Estimates from Tempest-D Observa...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chandrasekar Radhakrishnan V. Chandrasekar Steven C. Reising Shannon T. Brown Electrical and Computer Engineering Colorado State University Fort Collins CO USA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

The principal objective of this study is to develop machine learning (ML) models, in particular a random forest (RF) classifier and an artificial neural network (ANN) regression model, for estimating surface rain rates on a global basis using brightness temperature (TB) observations from the Temporal Experiment for Storms and Tropical Systems Demonstration (TEMPEST-D) CubeSat. The accuracy of the models is assessed by comparing the estimated rain rates with the Integrated Multi-satellitE Retrievals for GPM (IMERG) final run rain rate product, which also serves as the ground truth for ML model development. The ML models are developed using a dataset consisting of TEMPEST-D observations of 12 tropical cyclones (TC) and the corresponding IMERG products from various locations around the globe, including the Atlantic, eastern/western Pacific and Indian Oceans. To evaluate the performance of the ML models, independent validation is conducted using Hurricane Isaac and Typhoon Hagibis. The structural similarity index measure (SSIM) is used to assess the similarity between the ML estimated rain rates and the IMERG products. For Hurricane Isaac, an SSIM score of 0.8 is achieved, indicating a strong resemblance to the IMERG product. Similarly, for Typhoon Hagibis, the SSIM score is 0.73, indicating quite good agreement with the IMERG rain rate.

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Exploring the use of deep learning in task-flexible ILC*

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Exploring the use of deep learning in task-flexible ILC*

American Control Conference (ACC)

作者： Anantha Sai Hariharan Vinjarapu Yorick Broens Hans Butler Roland Tóth Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands ASML Veldhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Hungary

Growing demands in today’s industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role. Nonetheless, conventional model-based feedforward approaches are no longer sufficient to satisfy the challenging performance requirements. An attractive method for systems with repetitive motion tasks is iterative learning control (ILC) due to its superior performance. However, for systems with non-repetitive motion tasks, ILC is generally not applicable, despite of some recent promising advances. In this paper, we aim to explore the use of deep learning to address the task flexibility constraint of ILC. For this purpose, a novel Task Analogy based Imitation Learning (TAIL)-ILC approach is developed. To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward learning.

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