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arXiv 2023年

作者： Zhang, Zheshen You, Chenglong Magaña-Loaiza, Omar S. Fickler, Robert De León-Montiel, Roberto J. Torres, Juan P. Humble, Travis Liu, Shuai Xia, Yi Zhuang, Quntao Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Quantum Photonics Laboratory Department of Physics & Astronomy Louisiana State University Baton RougeLA70803 United States Tampere University Photonics Laboratory Physics Unit TampereFI-33720 Finland Instituto de Ciencias Nucleares Universidad Nacional Autónoma de México Apartado Postal 70-543 Cd. Mx.04510 Mexico ICFO - Institut de Ciencies Fotoniques Mediterranean Technology Park Castelldefels Barcelona08860 Spain Department of Signal Theory and Communications Universitat Politecnica de Catalunya Campus Nord D3 Barcelona08034 Spain Oak Ridge National Laboratory Oak RidgeTN United States Lausanne Switzerland Ming Hsieh Department of Electrical and Computer Engineering Department of Physics and Astronomy University of Southern California CA90089 United States

Entanglement is a quintessential quantum mechanical phenomenon with no classical equivalent. First discussed by Einstein, Podolsky, and Rosen and formally introduced by Schrödinger in 1935, entanglement has grown from a scientific debate to a radically new resource that sparks a technological revolution. This review focuses on the fundamentals and recent advances in entanglement-based quantum information technology (QIT), specifically in photonic systems. Photons are unique quantum information carriers with several advantages, such as their ability to operate at room temperature, their compatibility with existing communication and sensing infrastructures, and the availability of readily accessible optical components. Photons also interface well with other solid-state quantum platforms. We will first provide an overview on entanglement, starting with an introduction to its development from a historical perspective followed by the theory for entanglement generation and the associated representative experiments. We will then dive into the applications of entanglement-based QIT for sensing, imaging, spectroscopy, data processing, and communication. Before closing, we will present an outlook for the architecture of the next-generation entanglement-based QIT and its prospective applications. © 2023, CC BY.

关键词： Photons

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Converged reconfigurable intelligent surface and mobile edge computing for space information networks

arXiv

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arXiv 2021年

作者： Cao, Xuelin Yang, Bo Huang, Chongwen Yuen, Chau Zhang, Yan Niyato, Dusit Han, Zhu Engineering Product Development Pillar Singapore University of Technology and Design Singapore Singapore College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking International Joint Innovation Center Zhejiang University China Department of Informatics University of Oslo Norway School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering University of Houston United States

Space information networks (SIN) are facing an ever-increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space communications. However, this imposes a new paradigm on the architecture design of the integrated SIN. Recently, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) are the most promising techniques, conceived to improve communication and computation capability by reconfiguring the wireless propagation environment and offloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap the double benefits of computation and communication. In this article, we propose an RIS-assisted collaborative MEC architecture for SIN and discuss its implementation. Then we present its potential benefits, major challenges, and feasible applications. Subsequently, we study different cases to evaluate the system data rate and latency. Finally, we conclude with a list of open issues in this research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing

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Enhancing Device-Free Gesture Recognition Capability of Mobile Communication signals

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IEEE Transactions on communications 2025年

作者： Wu, Jingmiao He, Weijie Sun, Kai Huang, Wei Zhang, Haijun Leung, Victor C. M. Inner Mongolia University School of Electronic Information Engineering Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing Hohhot010021 China University of Science and Technology at Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Device-free gesture recognition using mobile communication signals is a convenient and efficient technology with broad application prospects in smart homes and human-computer interaction. It utilizes the effect of gestures on surrounding signals to achieve gesture recognition. The cell-specific reference signals (CRS) information can be used to achieve the task in close-range training scenarios. However, when gestures are performed at long-range or in non-training scenarios, the recognition performance will significantly degrade. To enhance device-free gesture recognition capability in arbitrary scenarios, we propose the signal quality enhancement algorithm and the gesture spectrogram construction method to solve this problem. Specifically, we superimpose the CRS information from multiple carriers to improve the gesture signal-to-noise ratio and increase the gesture sensing range. Then, we extract the gesture dynamic components from the CRS information and construct gesture spectrograms to represent scenario-independent gesture motion patterns. Using the gesture spectrogram features, we design a deep network to accomplish the gesture recognition task. We built a prototype system on a software-defined radio platform. Experimental results show that our proposed method can effectively increase the gesture sensing range from 30m² to 228m² and achieve an average recognition accuracy of 82.5% for five types of gestures in arbitrary scenarios. © 1972-2012 IEEE.

关键词： Gesture recognition

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ScanMix: Learning from Severe label Noise via Semantic Clustering and Semi-Supervised Learning

arXiv

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arXiv 2021年

作者： Sachdeva, Ragav Cordeiro, Filipe Rolim Belagiannis, Vasileios Reid, Ian Carneiro, Gustavo Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom School of Computer Science Australian Institute for Machine Learning Australia Visual Computing Lab Department of Computing Universidade Federal Rural de Pernambuco Brazil Otto-von-Guericke-Universität Magdeburg Germany Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

We propose a new training algorithm, ScanMix, that explores semantic clustering and semi-supervised learning (SSL) to allow superior robustness to severe label noise and competitive robustness to non-severe label noise problems, in comparison to the state of the art (SOTA) methods. ScanMix is based on the expectation maximisation framework, where the E-step estimates the latent variable to cluster the training images based on their appearance and classification results, and the M-step optimises the SSL classification and learns effective feature representations via semantic clustering. We present a theoretical result that shows the correctness and convergence of ScanMix, and an empirical result that shows that ScanMix has SOTA results on CIFAR-10/-100 (with symmetric, asymmetric and semantic label noise), Red Mini-ImageNet (from the Controlled Noisy Web labels), Clothing1M and WebVision. In all benchmarks with severe label noise, our results are competitive to the current SOTA. © 2021, CC BY.

关键词： Semantics

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LongReMix: Robust Learning with High Confidence Samples in a Noisy label Environment

arXiv

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arXiv 2021年

作者： Cordeiro, Filipe R. Sachdeva, Ragav Belagiannis, Vasileios Reid, Ian Carneiro, Gustavo School of Computer Science Australian Institute for Machine Learning Australia Visual Geometry Group Department of Engineering Science University of Oxford United Kingdom Visual Computing Lab Department of Computing Universidade Federal Rural de Pernambuco Brazil Otto-von-Guericke-Universität Magdeburg Germany Centre for Vision Speech and Signal Processing University of Surrey United Kingdom

State-of-the-art noisy-label learning algorithms rely on an unsupervised learning to classify training samples as clean or noisy, followed by a semi-supervised learning (SSL) that minimises the empirical vicinal risk using a labelled set formed by samples classified as clean, and an unlabelled set with samples classified as noisy. The classification accuracy of such noisy-label learning methods depends on the precision of the unsupervised classification of clean and noisy samples, and the robustness of SSL to small clean sets. We address these points with a new noisy-label training algorithm, called LongReMix, which improves the precision of the unsupervised classification of clean and noisy samples and the robustness of SSL to small clean sets with a two-stage learning process. The stage one of LongReMix finds a small but precise high-confidence clean set, and stage two augments this high-confidence clean set with new clean samples and oversamples the clean data to increase the robustness of SSL to small clean sets. We test LongReMix on CIFAR-10 and CIFAR-100 with introduced synthetic noisy labels, and the real-world noisy-label benchmarks CNWL (Red Mini-ImageNet), WebVision, Clothing1M, and Food101-N. The results show that our LongReMix produces significantly better classification accuracy than competing approaches, particularly in high noise rate problems. Furthermore, our approach achieves state-of-the-art performance in most datasets. The code is available at https://***/filipe-research/LongReMix. © 2021, CC BY.

关键词： Deep learning

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AI-assisted MAC for reconfigurable intelligent surface-aided wireless networks: Challenges and opportunities

arXiv

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arXiv 2021年

作者： Cao, Xuelin Yang, Bo Huang, Chongwen Yuen, Chau Renzo, Marco Di Han, Zhu Niyato, Dusit Poor, H. Vincent Hanzo, Lajos Engineering Product Development Pillar Singapore University of Technology and Design Singapore Singapore Zhejiang Provincial Key Lab of information processing Communication and networking Zhejiang University China Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France Department of Electrical and Computer Engineering University of Houston United States School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Department of Electrical Engineering Princeton University United States School of Electronics and Computer Science University of Southampton United Kingdom

Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation environment by exploiting the unique properties of metamaterials-based integrated large arrays of inexpensive antennas. Existing research demonstrates that RISs significantly improve the physical layer performance, including the wireless coverage, achievable data rate and energy efficiency. However, the medium access control (MAC) of multiple users accessing an RIS-enabled channel is still in its infancy, while many open issues remain to be addressed. In this article, we present four typical RIS-aided multi-user scenarios with special emphasis on the MAC schemes. We then propose and elaborate upon centralized, distributed and hybrid artificial-intelligence (AI)-assisted MAC architectures in RIS-aided multi-user communications systems. Finally, we discuss some challenges, perspectives and potential applications of RISs as they are related to MAC design. Copyright © 2021, The Authors. All rights reserved.

关键词： Medium access control

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Edge Learning for B5G Networks with Distributed signal processing: Semantic Communication, Edge Computing, and Wireless Sensing

arXiv

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arXiv 2022年

作者： Xu, Wei Yang, Zhaohui Ng, Derrick Wing Kwan Levorato, Marco Eldar, Yonina C. Debbah, Mérouane The National Mobile Communications Research Lab Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The School of Electrical Engineering and Telecommunications The University of New South Wales Australia The Department of Computer Science University of California IrvineCA92697 United States The Faculty of Math and CS Weizmann Institute of Science Rehovot7610001 Israel The Technology Innovation Institute The Mohamed Bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi9639 United Arab Emirates

To process and transfer large amounts of data in emerging wireless services, it has become increasingly appealing to exploit distributed data communication and learning. Specifically, edge learning (EL) enables local model training on geographically disperse edge nodes and minimizes the need for frequent data exchange. However, the current design of separating EL deployment and communication optimization does not yet reap the promised benefits of distributed signal processing, and sometimes suffers from excessive signalling overhead, long processing delay, and unstable learning convergence. In this paper, we provide an overview on practical distributed EL techniques and their interplay with advanced communication optimization designs. In particular, typical performance metrics for dual-functional learning and communication networks are discussed. Also, recent achievements of enabling techniques for the dual-functional design are surveyed with exemplifications from the mutual perspectives of "communications for learning" and "learning for communications." The application of EL techniques within a variety of future communication systems are also envisioned for beyond 5G (B5G) wireless networks. For the application in goal-oriented semantic communication, we present a first mathematical model of the goal-oriented source entropy as an optimization problem. In addition, from the viewpoint of information theory, we identify fundamental open problems of characterizing rate regions for communication networks supporting distributed learning-and-computing tasks. We also present technical challenges as well as emerging application opportunities in this field, with the aim of inspiring future research and promoting widespread developments of EL in B5G. Copyright © 2022, The Authors. All rights reserved.

关键词： Electronic data interchange

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Interferometric radar for activity recognition and benchmarking in different radar geometries 5

Interferometric radar for activity recognition and benchmark...

引用

5th IET International Radar Conference, IET IRC 2020

作者： Zhou, Boyu Le Kernec, Julien Yang, Shufan Fioranelli, Francesco Romain, Olivier Zhao, Zhiqin Department of Computer Science The University of Hong Kong Hong Kong Hong Kong School of Information and Communication University of Electronic Science and Technology of China Chengdu China James Watt School of Engineering University of Glasgow Glasgow United Kingdom ETIS – Signal and Information Processing lab University Cergy-Pontoise Cergy France MS3-Microwave Sensing Signals and Systems TU Delft Delft Netherlands

ISBN: (纸本)9781839535406

Radar micro-Doppler signatures have been proposed for human activity classification for surveillance and ambient assisted living in healthcare-related applications. A known issue is the performance reduction when the target is moving tangentially to the line-of-sight of the radar. Multiple techniques have been proposed to address this, such as multistatic radar and to some extent, interferometric radar. A simulator is presented to generate synthetic data representative of 8 different radar systems (including configurations as monostatic, multistatic, and interferometric) to quantify classification performances as a function of aspect angles and deployment geometries. This simulator allows an unbiased performance evaluation of the different radar systems. 6 human activities are considered with signatures originating from motion-captured data of 14 different subjects. The results show that interferometric radar data with fusion outperforms the other methods with over 97.6% accuracy consistently across all aspect angles, as well as the potential for simplified indoor deployment. © 2020 IET Conference Proceedings. All rights reserved.

关键词： Machine learning

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Rethinking online action detection in untrimmed videos: A novel online evaluation protocol

arXiv

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arXiv 2020年

作者： Baptista-Ríos, Marcos López-Sastre, Roberto J. Caba Heilbron, Fabian van Gemert, Jan C. Acevedo-Rodríguez, F. Javier Maldonado-Bascón, Saturnino GRAM Department of Signal Theory and Communications University of Alcalá Alcalá de Henares Spain Adobe Research Media Intelligence Lab Deep Learning Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

The Online Action Detection (OAD) problem needs to be revisited. Unlike traditional offline action detection approaches, where the evaluation metrics are clear and well established, in the OAD setting we find very few works and no consensus on the evaluation protocols to be used. In this work we propose to rethink the OAD scenario, clearly defining the problem itself and the main characteristics that the models which are considered online must comply with. We also introduce a novel metric: the Instantaneous Accuracy (IA). This new metric exhibits an online nature and solves most of the limitations of the previous metrics. We conduct a thorough experimental evaluation on 3 challenging datasets, where the performance of various baseline methods is compared to that of the state-of-the-art. Our results confirm the problems of the previous evaluation protocols, and suggest that an IA-based protocol is more adequate to the online scenario. The baselines models and a development kit with the novel evaluation protocol are publicly available here (https://***/gramuah/ia). Copyright © 2020, The Authors. All rights reserved.

关键词： computer vision

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Antenna Array Enabled Space/Air/Ground communications and Networking for 6G

arXiv

引用

arXiv 2021年

作者： Xiao, Zhenyu Han, Zhu Nallanathan, Arumugam Dobre, Octavia A. Clerckx, Bruno Choi, Jinho He, Chong Tong, Wen The School of Electronic and Information Engineering Beihang University Beijing100191 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom The Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada The Communications and Signal Processing Group Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The School of Information Technology Deakin University GeelongVIC3220 Australia The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The Wireless Advanced System Competency Centre Huawei Technologies Co. Ltd. OttawaONK2K 3J1 Canada

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays (e.g., phased arrays, digital arrays, and reconfigurable intelligent surfaces, etc.) with various beamforming technologies (e.g., analog beamforming, digital beamforming, hybrid beamforming, and passive beamforming, etc.) in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/ai

关键词： Beamforming

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