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A Tutorial on Movable Antennas for Wireless Networks

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arXiv 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Chengdu611731 China National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electronic and Information Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada State Key Laboratory of CNS/ATM MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. We also highlight promising research directions in this area to inspire future investigatio

关键词： Antenna arrays

A cross Transformer for image denoising

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arXiv 2023年

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

A Learning-based Honeypot Game for Collaborative Defense in UAV Networks

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A Learning-based Honeypot Game for Collaborative Defense in ...

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yuntao Wang Zhou Su Abderrahim Benslimane Qichao Xu Minghui Dai Ruidong Li School of Cyber Science and Engineering Xi'an Jiaotong University China Laboratory of Computer Sciences Avignon University France School of Mechatronic Engineering and Automation Shanghai University China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Department of Electrical and Computer Engineering Kanazawa University Japan

ISBN: (纸本)9781665435413

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs' participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the pres-ence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fair-ness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV's collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions.

关键词： Costs Heuristic algorithms Collaboration Games Resists Reinforcement learning Autonomous aerial vehicles

Evaluating and Constraining Hardware Assertions with Absent Scenarios

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Journal of computer science & Technology 2020年第5期35卷 1198-1216页

作者： Hui-Na Chao Hua-Wei Li Xiaoyu Song Tian-Cheng Wang Xiao-Wei Li State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518052China Department of Electrical and Computing Engineering Portland State UniversityPortlandOR 97207U.S.A.

Mining from simulation data of the golden model in hardware design verification is an effective solution to assertion *** the simulation data is inherently incomplete,it is necessary to evaluate the truth values of the mined *** paper presents an approach to evaluating and constraining hardware assertions with absent scenarios.A Belief-fail Rate metric is proposed to predict the truth/falseness of generated *** considering both the occurrences of free variable assignments and the conflicts of absent scenarios,we use the metric to sort true assertions in higher ranking and false assertions in lower *** Belief-failRate guided assertion constraining method leverages the quality of generated *** experimental results show that the Belief-failRate framework performs better than the existing *** addition,the assertion evaluating and constraining procedure can find more assertions that cover new design functionality in comparison with the previous methods.

关键词： hardware formal verification assertion generation data mining assertion evaluation assertion coverage

Learning-Based Calibration of Flux Crosstalk in Transmon Qubit Arrays

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Physical Review Applied 2023年第2期20卷 024070-024070页

作者： Cora N. Barrett Amir H. Karamlou Sarah E. Muschinske Ilan T. Rosen Jochen Braumüller Rabindra Das David K. Kim Bethany M. Niedzielski Meghan Schuldt Kyle Serniak Mollie E. Schwartz Jonilyn L. Yoder Terry P. Orlando Simon Gustavsson Jeffrey A. Grover William D. Oliver Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Wellesley College Wellesley Massachusetts 02481 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA MIT Lincoln Laboratory Lexington Massachusetts 02421 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Superconducting quantum processors comprising flux-tunable data and coupler qubits are a promising platform for quantum computation. However, magnetic flux crosstalk between the flux-control lines and the constituent qubits impedes precision control of qubit frequencies, presenting a challenge to scaling this platform. In order to implement high-fidelity digital and analog quantum operations, one must characterize the flux crosstalk and compensate for it. In this work, we introduce a learning-based calibration protocol and demonstrate its experimental performance by calibrating an array of 16 flux-tunable transmon qubits. To demonstrate the extensibility of our protocol, we simulate the crosstalk matrix learning procedure for larger arrays of transmon qubits. We observe an empirically linear scaling in calibration time with system size while maintaining a median qubit frequency error below 300 kHz.

关键词： Machine learning Optoelectronics Quantum control Quantum gates Quantum information with solid state qubits Superconducting qubits Microwave techniques

Demonstration of a Polarization-Agnostic Geometric Phase in Nonlocal Metasurfaces

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arXiv 2023年

作者： Overvig, Adam Kasahara, Yoshiaki Xu, Gengyu Alù, Andrea Photonics Initiative Advanced Science Research Center Graduate Center of the City University of New York United States Department of Electrical and Computer Engineering The University of Texas Austin United States Physics Program Graduate Center City University of New York United States

Symmetry-driven phenomena arising in nonlocal metasurfaces supporting quasi-bound states in the continuum (q-BICs) have been opening new avenues to tailor enhanced light-matter interactions via perturbative design principles. Geometric phase concepts—observed in many physical systems—are particularly useful in nonlocal metasurfaces, as they enable to locally pattern the q-BIC scattering rate and phase across the metasurface aperture without affecting the delocalized nature of the q-BIC resonance. However, this control typically comes with stringent limitations in terms of efficiency and/or of polarization operation. Here, we unveil a new form of geometric phase control, accumulated along a continuous contour of geometric perturbations that parametrically encircle a singularity associated with a bound state. This response is obtained regardless of the chosen polarization state, which can be rationally specified by a judicious choice of the perturbation geometry. Our findings extend geometric phase concepts to arbitrary polarizations, including linear and elliptical states, with near-unity scattering efficiency. This polarization-agnostic geometric phase offers new opportunities for wavefront manipulation, multifunctional operation and light emission, with applications in augmented reality, secure communications, wireless systems, imaging, lasing, nonlinear and quantum optics. © 2023, CC BY.

关键词： Polarization

Self-supervised On-device Federated Learning from Unlabeled Streams

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arXiv 2022年

作者： Shi, Jiahe Wu, Yawen Zeng, Dewen Tao, Jun Hu, Jingtong Shi, Yiyu The State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai200433 China The Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States

The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unlabeled data to continuously improve accuracy. Self-supervised representation learning has achieved promising performances using centralized unlabeled data. However, the increasing awareness of privacy protection limits centralizing the distributed unlabeled image data on edge devices. While federated learning has been widely adopted to enable distributed machine learning with privacy preservation, without a data selection method to efficiently select streaming data, the traditional federated learning framework fails to handle these huge amounts of decentralized unlabeled data with limited storage resources on edge. To address these challenges, we propose a Self-supervised On-device Federated learning framework with coreset selection, which we call SOFed, to automatically select a coreset that consists of the most representative samples into the replay buffer on each device. It preserves data privacy as each client does not share raw data while learning good visual representations. Experiments demonstrate the effectiveness and significance of the proposed method in visual representation learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Data privacy

A Smart Capacity Enhancement and Estimation Model for Hybrid Buildings by using Light Weight Deep Learning Model

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A Smart Capacity Enhancement and Estimation Model for Hybrid...

Disruptive Technologies (ICDT), International Conference on

作者： Senthil Kumar Nageswara Rao Atyam Khadar Nawas K Shashank Awasthi M. Kotteeswaran K. S. Thirunavukkarasu KL School of Architecture Koneru Lakshmanan Education Foundation Guntur Andhra Pradesh India Electrical and Electronics Department CMR Institute of Technology Bengaluru Karnataka India School of Computing Science and Engineering Vellore Institute of Technolog Chennai Tamil Nadu India Department of Computer Science and Engineering GL Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India School of Management and Commerce Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India School of Computing Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India

Generally, for the buildings constructed in our areas, the conventional foundation known as 'shallow', i.e. from the ground level downwards, is set up in three dimensions such as length, width and depth. Foundations are not the same for all types of structures. The size and structure of the foundation varies depending on the nature of the prevailing soil, ground water level of the plot, type of building, and total load of the structure. In this paper an innovation estimation model was proposed based on light weigth deep learning technique. The proposed model introduces capacity estimation and energy efficiency plans and designs. These are all relating to commercial buildings, craft facilities and mass housing projects to enable design, construction. It also maintains the buildings under minimum energy consumption without stressing the operation of buildings. The health and comfort of the occupants are establishing compliance standards and ensure the maximum standards for energy efficiency in the plans or designs of commercial buildings. The proposed model also focuses the capacity estimation and energy efficiency programs beyond maximum standards.

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Wideband Circularly Polarized Sequentially Rotated Microstrip Antenna Array with Sequential-Phase Feeding Network

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Journal of Communications and Information Networks 2020年第3期5卷 350-357页

作者： Meijiao Sun Nengwu Liu Lei Zhu Guang Fu National Key Laboratory of Antennas and Microwave Technology Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering Faculty of Science and TechnologyUniversity of MacaoMacao 999078China State Key Laboratory of Millimeter Waves Nanjing 210096China

A wideband 2×2 sequential-phase fed circularly polarized(CP)microstrip antenna array(MAA)with sequentially rotated elements is ***,the antenna element loaded with shorting pins is studied as compared to the traditional ***,a feeding network is designed to be connected together in a sequential rotation manner,resulting in forming a four-port network to allow symmetrical positioning of CP microstrip patch *** that,straight strips are added to the feeding network in order to increase the distance and reduce coupling between array *** also play a role in improving the impedance matching of the ***,the proposed antenna array is designed and *** results are presented that its impedance bandwidth is about 36.3%at 24.1~34.8 GHz for|S_(11)|<10 dB and axial-ratio(AR)bandwidth is about 20.4%at 24.2~29.7 GHz for AR<3 ***,a peak gain of 11.1 dBic is simulated over the operating frequency range,which is significantly improved compared to the classic and existing works.

关键词： microstrip antenna array sequential-phase feeding network straight strips shorting pins