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Fundamental Limits of Multiple-Access Integrated Sensing and communication systems

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arXiv 2022年

作者： Liu, Yao Li, Min Liu, An Ong, Lawrence Yener, Aylin The College of Information Science and Electronic Engineering The Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou310027 China The School of Engineering University of Newcastle CallaghanNSW2308 Australia The Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States

A memoryless state-dependent multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the sensing state sequences through echo signals. In particular, the sensing states are assumed to be correlated with the channel state, and the receiver has imperfect channel state information. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression via distributed Wyner-Ziv coding at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results. © 2022, CC BY.

关键词： Transmitters

A Two-way Automatic Alignment Visible Light communication system Based on Red Laser

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A Two-way Automatic Alignment Visible Light Communication Sy...

IEEE International Conference on Electrical engineering and Photonics (EExPolytech)

作者： Han Zhang Jiaming Li Zhen Liu Hongming Zhang Department of Electronic Engineering Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing China Zhengzhou Power Supply Company State Grid Henan Electric Power Company Zhengzhou China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen China

Visible light communication(VLC) has shown in explosive growth recent years, and LEDs based visible light wireless communication has the potential to change lives in the future with applications such as LiFi, smart lighting and indoor positioning. Due to the low bandwidth of LED devices, the VLC systems using LDs(laser diodes) as light sources can achieve faster transmission speed. In this paper, we propose a high-speed visible light communication system with automatic alignment function using red laser. Each tranceiver in the system can be moved in the angle of view of another, and be aligned in real time using cameras, computers and two-dimensional turntable. TCP data transmission rate more than 700 Mbps with a distance of 6 m free-space link, $\pm 10^{\circ}\text{angle}$ of view was achieved.

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High-performance III-V/Si3N4 O-band External Cavity Tunable Laser

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High-performance III-V/Si3N4 O-band External Cavity Tunable ...

Conference on Lasers and Electro-Optics (CLEO)

作者： Yuyao Guo Xinhang Li Minhui Jin Chuxin Liu Weihan Xu Liangjun Lu Jingya Xie Jianping Chen Linjie Zhou Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu China Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai China

We present a III-V/Si 3 N 4 O-band hybrid laser, which shows a high output power of 42 mW, wide tuning range of 62 nm with side-mode suppression ratio >50 dB, and narrow intrinsic linewidth of ~600 Hz.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Power lasers Lasers and electrooptics Hybrid power systems Laser tuning Electrooptical waveguides Power generation

Reconfigurable Intelligent Surface (RIS)-aided Vehicular Networks: Their Protocols, Resource Allocation, and Performance

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arXiv 2022年

作者： Chen, Yuanbin Wang, Ying Zhang, Jiayi Zhang, Ping Hanzo, Lajos State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China 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 The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Broadband Communication Peng Cheng Laboratory Shenzhen518055 China The Department of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Reconfigurable intelligent surfaces (RISs) assist in paving the way for the evolution of conventional vehicular networks to autonomous driving. Having said that, the 3rd Generation Partnership Project (3GPP) faces numerous open challenges concerning the RIS-aided vehicle-to-everything (V2X) solutions of the near future. To tackle these challenges and to stimulate future research, this article focuses on the prospective transmission design of RIS-aided V2X communications. In particular, two V2X sidelink modes are enhanced by exploiting RISs and their variants, followed by a customized transmission frame structure that partitions the transmission efforts into different phases. Next, effective channel tracking and resource allocation techniques are developed for attaining a high beamforming gain at low overhead and complexity. Finally, promising research topics are highlighted and future 3GPP standardization items are proposed for RIS-aided V2X systems. © 2022, CC BY-NC-ND.

关键词： Vehicle to Everything

A review:Photonics devices,architectures,and algorithms for optical neural computing

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Journal of Semiconductors 2021年第2期42卷 64-79页

作者： Shuiying Xiang Yanan Han Ziwei Song Xingxing Guo Yahui Zhang Zhenxing Ren Suhong Wang Yuanting Ma Weiwen Zou Bowen Ma Shaofu Xu Jianji Dong Hailong Zhou Quansheng Ren Tao Deng Yan Liu Genquan Han Yue Hao State Key Laboratory of Integrated Service Networks Xidian UniversityXi'an 710071China State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology School of MicroelectronicsXidian UniversityXi’an 710071China State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(iMLic)Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Wuhan National Laboratory for Optoelectronics School of Optical and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074China School of Electronics Engineering and Computer Science Peking UniversityBeijing 100871China School of Physical Science and Technology Southwest UniversityChongqing 400715China

The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore *** neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed,wide bandwidth,and massive ***,we offer a review on the optical neural computing in our research groups at the device and system *** photonics neuron and photonics synapse plasticity are *** addition,we introduce several optical neural computing architectures and algorithms including photonic spiking neural network,photonic convolutional neural network,photonic matrix computation,photonic reservoir computing,and photonic reinforcement ***,we summarize the major challenges faced by photonic neuromorphic computing,and propose promising solutions and perspectives.

关键词： photonics neuron photonic STDP photonic spiking neural network optical reservoir computing optical convolutional neural network neuromorphic photonics

Distributed machine learning for wireless communication networks: Techniques, architectures, and applications

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arXiv 2020年

作者： Hu, Shuyan Chen, Xiaojing Ni, Wei Hossain, Ekram Wang, Xin State Key Laboratory of ASIC and System School of Information Science and Technology Fudan University Shanghai200433 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China SydneyNSW2122 Australia Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T 5V6 Canada State Key Laboratory of ASIC and System Shanghai Institute for Advanced Communication and Data Science Department of Communication Science and Engineering Fudan University Shanghai200433 China

Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved capabilities of terminal devices, explosively growing data volume, congestion in the radio interfaces, and increasing concern of data privacy. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and massive amount of data, give rise to new challenges in the design of DML techniques. There is a clear gap in the existing literature in that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks. Specifically, we review the latest applications of DML in power control, spectrum management, user association, and edge cloud computing. The optimality, scalability, convergence rate, computation cost, and communication overhead of DML are analyzed. We also discuss the potential adversarial attacks faced by DML applications, and describe state-of-the-art countermeasures to preserve privacy and security. Last but not least, we point out a number of key issues yet to be addressed, and collate potentially interesting and challenging topics for future research. Copyright © 2020, The Authors. All rights reserved.

关键词： Data privacy

P-66: Simulation and Optimization of In-Situ Integration Ultraviolet HEMT-LED Enabled by P-GaN Selective Epitaxy Growth

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SID Symposium Digest of Technical Papers 2023年第1期54卷

作者： Jingyang Zhang Wei Huang Zhaojun Liu Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen Guangdong China 518055 A State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics & Systems School of Microelectronics Fudan University Shanghai China 200433

Based on previous work, the ultraviolet HEMT-LED we fabricated by selective epitaxy growth, this paper further performed simulation to explore the most appropriate p-GaN selection size. P-GaN grown by Selective Epitaxy Growth (SEG) can not only be employed as a junction termination to reduce the electric field crowding of the gate of HEMT, but also form an active region by contacting with 2-dimentional electron gas (2DEG). The simulation results provide an effective reference for the design of p-GaN grown by SEG, and further pave the way for the realization of the idea of integration through the selection of growth methods.

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Conceptual Study and Performance Analysis of Tandem Dual-Antenna Spaceborne SAR Interferometry

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arXiv 2023年

作者： Hu, Fengming Xu, Feng Qiu, Xiaolan Ding, Chibiao Jin, Yaqiu Wang, Robert Fudan University Shanghai200433 China The Department of Space Microwave Remote Sensing System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing101408 China The National Key Laboratory of Science and Technology on Microwave Imaging Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Multi-baseline synthetic aperture radar interferometry (MB-InSAR), capable of mapping 3D surface model with high precision, is able to overcome the ill-posed problem in the single-baseline InSAR by use of the baseline diversity. Single-pass MB acquisition with the advantages of high coherence and simple phase components has a more practical capability in 3D reconstruction than conventional repeat-pass MB acquisition. Using an asymptotic 3D phase unwrapping (PU), it is possible to get a reliable 3D reconstruction using very sparse acquisitions but the interferograms should follow the optimal baseline design. However, current spaceborne SAR system doesn’t satisfy this principle, inducing more difficulties in practical phase unwrapping. In this article, a new concept of Tandem Dual-Antenna SAR Interferometry (TDA-InSAR) system for acquiring optimal single-pass multibaseline (MB) interferograms is proposed. Two indicators, i.e., expected relative height precision and successful phase unwrapping rate, are selected to optimize the system parameters. Additionally, the performances of various baseline configurations in typical scenarios and the impact of different error sources are investigated correspondingly. The simulation-based demonstrations shows that the proposed TDA-InSAR enables the fast 3D reconstruction in a single flight and optimal MB acquisitions for asymptotic 3D phase unwrapping. © 2023, CC BY.

关键词： Synthetic aperture radar

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics