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arXiv 2020年

作者： Hao, Wanming Sun, Gangcan Zhou, Fuhui Mi, De Shi, Jia Xiao, Pei Leung, Victor C.M. School of Information Engineering Zhengzhou University Zhengzhou450001 China 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210000 China State Key Laboratory of Integrated Services Networks Xi'dian University Xian710071 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T1Z4 Canada

In this paper, we investigate the energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. We adopt two sparse radio frequency chain antenna structures at the base station (BS), i.e., fully-connected and subarray structures, and design the codebook-based analog precoding according to the different structures. Then, we formulate a joint digital multicast, unicast precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at the BS and minimum harvested energy at receivers are considered. Due to its difficulty to directly solve the formulated problem, we equivalently transform the fractional objective function into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques and propose an iterative algorithm to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop a zero forcing (ZF) technique-based low complexity iterative algorithm. Specifically, the ZF technique is applied to cancel the inter-unicast interference and the first order Taylor approximation is used for the convexification of the non-convex constraints in the original problem. Finally, simulation results are provided to compare the performance of the proposed algorithms under different schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention networks

arXiv

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arXiv 2024年

作者： Li, Xiaojie Zhang, Songyang Li, Hang Li, Xiaoyang Xu, Lexi Xu, Haigao Mei, Hui Zhu, Guangxu Qi, Nan Xiao, Ming Shenzhen Research Institute of Big Data The Chinese University of Hong Kong-Shenzhen Guangdong China College of Physics Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70503 United States Research Institute China United Network Communications Corporation Beijing100048 China China Mobile Communications Group Jiangxi Co. Ltd 330038 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing210016 China State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China Department of Information Science and Engineering School of Electrical Engineering of KTH Royal Institute of Technology Stockholm Sweden

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MBRMR within a single area, eliminating the need for multiregion datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multiband data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework's adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT's enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT's effectiveness and practicality for MBRMR in environments with limited data availability. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Hybrid optical pumping of K and Rb atoms in a paraffin coated vapor cell

arXiv

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arXiv 2017年

作者： Li, Wenhao Peng, Xiang Budker, Dmitry Wickenbrock, Arne Pang, Bo Zhang, Rui Guo, Hong State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China Helmholtz Institute Mainz Mainz55099 Germany Department of Physics University of California BerkeleyCA94720-7300 United States Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Johannes Gutenberg-University Mainz Mainz55128 Germany College of Science Interdisciplinary Center for Quantum Information National University of Defense Technology Changsha410073 China

Dynamic hybrid optical pumping effects with a radio-frequency-field-driven nonlinear magneto-optical rotation (RF NMOR) scheme are studied in a dual-species paraffin coated vapor cell. By pumping K atoms and probing 87Rb atoms, we achieve an intrinsic magnetic resonance linewidth of 3 Hz and the observed resonance is immune to power broadening and light-shift effects. Such operation scheme shows favorable prospects for atomic magnetometry applications. Copyright © 2017, The Authors. All rights reserved.

关键词： Optical pumping

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The Synchronized Peer-to-Peer Framework and Distributed Contention-Free Medium Access for Multihop Wireless Sensor networks

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Journal of Sensors 2009年第1期2008卷

作者： Ahmad Khayyat Ahmed Safwat Laboratory for Advanced Wireless Networks Department of Electrical and Computer Engineering Queen′s University Kingston ON Canada K7L 3N6queensu.ca

IEEE 802.15.4 is a low-power, low-rate MAC/PHY standard that meets most of the stringent requirements of singlehop wireless sensor networks. Sensor networks with nodal populations composed of thousands of devices have been envisioned in conjunction with environmental, vehicular, military applications, and many others. However, such large sensor network deployments necessitate multihop support as well as low power consumption. In the light of the standard′s extremely limited joint support of the two aforementioned attributes, this paper presents two essential contributions. First, a framework is proposed to implement a new IEEE 802.15.4 operating mode, namely, the synchronized peer-to-peer mode. This mode is designed to enable the standard′s low-power features in peer-to-peer multihop-ready topologies. The second contribution is a distributed GTS (dGTS) management scheme designed to function in the newly devised network mode. This protocol provides reliable contention-free access in peer-to-peer topologies in a completely distributed manner. Assuming optimal routing, our simulation experiments reveal perfect delivery ratios as long as the traffic load does not reach or surpass its saturation threshold. dGTS sustains at least twice the delivery ratio of contention-based access under suboptimal dynamic routing. Moreover, the dGTS scheme exhibits minimum power consumption by eliminating the retransmissions attributed to contention, which, in turn, reduces the number of transmissions to a minimum.

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Multi-Sources Fusion Learning for Multi-Points NLOS Localization in OFDM System

arXiv

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arXiv 2024年

作者： Wang, Bohao Shuai, Zitao Huang, Chongwen Yang, Qianqian Yang, Zhaohui Jin, Richeng Hammadi, Ahmed Al Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China The State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Hangzhou310027 China The Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2122 United States The Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates The School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Khalifa University of Science and Technology PO Box 127788 Abu Dhabi United Arab Emirates

Accurate localization of mobile terminals is a pivotal aspect of integrated sensing and communication systems. Traditional fingerprint-based localization methods, which infer coordinates from channel information within pre-set rectangular areas, often face challenges due to the heterogeneous distribution of fingerprints inherent in non-line-of-sight (NLOS) scenarios, particularly within orthogonal frequency division multiplexing systems. To overcome this limitation, we develop a novel multi-sources information fusion learning framework referred to as the Autosync Multi-Domains NLOS Localization (AMDNLoc). Specifically, AMDNLoc employs a two-stage matched filter fused with a target tracking algorithm and iterative centroid-based clustering to automatically and irregularly segment NLOS regions, ensuring uniform distribution within channel state information across frequency, power, and time-delay domains. Additionally, the framework utilizes a segment-specific linear classifier array, coupled with deep residual network-based feature extraction and fusion, to establish the correlation function between fingerprint features and coordinates within these regions. Simulation results reveal that AMDNLoc achieves an impressive NLOS localization accuracy of 1.46 meters on typical wireless artificial intelligence research datasets and demonstrates significant improvements in interpretability, adaptability, and scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

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Power Quality Signal Analysis for the Smart Grid Using the Hilbert-Huang Transform

Power Quality Signal Analysis for the Smart Grid Using the H...

引用

IEEE Pacific Rim Conference on Communications, computers and Signal Processing

作者： Zhixiong Cai Fan Ning Wei Li T. Aaron Gulliver Beijing University of Posts and Telecommunications Laboratory of Communication and Sensor Networks Beijing 100876 P. R. China Department of Electrical and Computer Engineering University of Victoria Victoria BC Canada V8W 2Y2

ISBN: (纸本)9781479915002

Power grids are currently undergoing a transformation to smart grids through the automation of the power distribution network. This paper considers the power quality in this network, and employs the Hilbert-Huang transform (HHT) to characterize power signal disturbances. Results are presented which show the advantages of the HHT in terms of data adaptivity and time-frequency resolution. The HHT clearly shows the underlying structure of the signal disturbances, which is essential for developing automatic disturbance detection and classification algorithms.

关键词： Smart grid Hilbert-Huang transform power quality signal analysis non-stationary analysis

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Investigating Bi-level optimization for learning and vision from a unified perspective: A survey and beyond

arXiv

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arXiv 2021年

作者： Liu, Risheng Gao, Jiaxin Zhang, Jin Meng, Deyu Lin, Zhouchen DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian116024 China Department of Mathematics Southern University of Science and Technology National Center for Applied Mathematics Shenzhen China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an Shaanxi China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchical structure, involving two levels of optimization tasks, where one task is nested inside the other. In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems, such as hyper-parameter optimization, multi-task and meta learning, neural architecture search, adversarial learning and deep reinforcement learning, actually all contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of BLO. Then we construct a best-response-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies, covering aspects ranging from fundamental automatic differentiation schemes to various accelerations, simplifications, extensions and their convergence and complexity properties. Last but not least, we discuss the potentials of our unified BLO framework for designing new algorithms and point out some promising directions for future research. © 2021, CC BY.

关键词： Deep learning

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Retraction Note: Placement delivery array design for the coded caching scheme in medical data sharing

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Neural Computing and Applications 2024年第18期36卷 1-1页

作者： Sun, Rong Zheng, Huihui Liu, Jingwei Du, Xiaojiang Guizani, Mohsen State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Xiamen Key Laboratory of Mobile Multimedia Communications Huaqiao University Xiamen China The Department of Computer and Information Sciences Temple University Philadelphia USA The Department of Electrical and Computer Engineering University of Idaho Moscow USA

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Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

arXiv

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arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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Editorial: Simulators and experimental testbeds design and development for wireless networks

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Eurasip Journal on Wireless Communications and Networking 2010年 2010卷

作者： Bader, Faouzi Chelius, Guillame Ibars, Christian Ibnkahla, Mohamed Passas, Nikos Rhiemeier, Arnd-Ragnar Access Technogies Research Area Parc Mediterrani de la Tecnologia Av. Canal Olimpic-Casstelldefels 08860 Barcelona Spain 46 allée d'Italie F-69364 Lyon Cedex 07 France Department of Electrical and Computer Engineering Queen's University Kingston ON K7L 3N6 Canada Communication Networks Laboratory Department of Informatics and Telecommunications University of Athens Panepistimiopolis Ilisia 15784 Athens Greece Defence and Security Defence Electronics EADS Deutschland GmbH 89070 Ulm Germany

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