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HY-PC:Enabling Consistent Positioning and Communication Using Visible Light

China Communications 2023年第4期20卷 180-194页

作者： Xiangyu Liu Yaoheng Chen Lei Guo Song Song School of Information Science and Engineering Shenyang Ligong UniversityShenyang 110158China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Hangzhou Institute of Advanced Technology Hangzhou 310056China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China

Visible light(VL)plays an important role in achieving high-precision positioning and low bit error radio(BER)data ***,most VL-based systems can not achieve positioning and communication,*** are two problems:1)the hybrid systems are difficult to extract distinguishable positioning beacon features without affecting communication performance,2)in the hybrid systems,the lost data bits in the inter-frame gap(IFG)are hard to recover,which affects positioning and communication ***,in this article,we propose a novel VL-based hybrid positioning and communication system,named HY-PC system,to solve the above ***,we propose the robust T-W mapping for recognizing specific Light Emitting Diodes(LEDs),which can provide stable LED recognition accuracy without adding extra beacon data and does not decrease the communication ***,we also propose the novel linear block coding and bit interleaving mechanism,which can recover the lost data bits in the IFG and improve data communication ***,we use commercial off-the-shelf devices to implement our HY-PC system,extensive experimental results show that our HY-PC system can achieve consistent high-precision positioning and low-BER data communication,simultaneously.

关键词： visible light positioning visible light communication T-W Mapping linear block coding bit interleaving

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SVPWM-Based Three-Phase DC/AC Talkative Power Converters 49

SVPWM-Based Three-Phase DC/AC Talkative Power Converters

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Leng, Yang Zhu, Rongwu Liserre, Marco Hoeher, Peter A. Beiranvand, Hamzeh Kiel University Chair of Power Electronics Kiel Germany Harbin Institute of Technology Department of Electrical Engineering Shenzhen China Kiel University Chair of Information and Coding Theory Kiel Germany

ISBN: (纸本)9798350331820

Talkative power conversion (TPC), which embeds information into the switching ripple to achieve the simultaneous transmission of power and data without installation of additional hardware, can reduce the additional communication infrastructure in cyber-physical systems of smart grid and renewable power plants. The TPC in DC/DC converters has been the subject of numerous investigations, but comparatively few studies have focused on the DC/AC converters. In this paper, the space-vector pulse width modulation (SVPWM)-based TPC techniques including both variable zero vector width modulation (VZVWM) and variable zero vector position modulation (VZVPM) for three-phase DC/AC converters are proposed. The DC/AC converters can transmit information to the receiver, crossing the LCL filter and power cable, with a good power quality. The sliding FFT method is employed for demodulation, and the bit rate is analyzed. Finally, simulations and experiments prove the viability of the SVPWM-based three-phase DC/AC talkative power converters with a bit rate of 100 bps and 1-blt length time delay. © 2023 IEEE.

关键词： Embedded systems

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An Indoor P2MP Narrow Beam Optical Wireless Communication (NB-OWC) System for Cloud Office Application

An Indoor P2MP Narrow Beam Optical Wireless Communication (N...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Zhang, Shan Li, Junwei Li, Jun You, Xiaodi Shen, Gangxiang Cheng, Qiang China Mobile Research Institute Department of Fundamental Network Technology Beijing China School of Electronic and Information Engineering Soochow University Suzhou China China Academy of Information and Communications Technology Broadband Network Research Department Beijing China

This paper demonstrates a P2MP NB-OWC system. Passive components plus integrated transmission and reception are implemented in optical antenna (OA). A cascaded relay architecture is designed for Cloud Office. The syst... 详细信息

ISBN: (纸本)9798350312614

关键词： beam alignment FTTR NB-OWC optical antenna P2MP system

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital Communications and networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-CommunicationsChofuTokyo 182-8585Japan Information Technology Center Nagoya UniversityNagoya 464-8601Japan National Institute of Informatics(NII) Tokyo 101-8430Japan Hohhot Minzu College Hohhot 010051China Toyota Motor Corporation Tokyo 112-8701Japan Department of Computer Science and Engineering Shanghai Jiaotong UniversityShanghai 200240China

Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central *** requirement outpaces the capacity of traditional communication *** tackle this,we propose a novel framework using semantic communications,through a region of interest semantic segmentation method,to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise *** solve the knowledge base inconsistencies inherent in semantic communications,we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge *** system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient ***,the implementation of blockchain sharding handles differentiated knowledge bases for various tasks,thus boosting overall blockchain *** results show a great reduction in latency by sharding and an increase in model accuracy,confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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AQROM:A quality of service aware routing optimization mechanism based on asynchronous advantage actor-critic in software-defined networks

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Digital Communications and networks 2024年第5期10卷 1405-1414页

作者： Wei Zhou Xing Jiang Qingsong Luo Bingli Guo Xiang Sun Fengyuan Sun Lingyu Meng School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China The 34th Research Institute of China Electronics Technology Group Corporation Guilin541004China Guangxi Key Laboratory of Optical Network and Optical Information Security Guilin541004China State Key Laboratory of Information Photonics and Optical Communications BeijingUniversity of Posts and TelecommunicationsBeijing100876China Department of Electrical and Computer Engineering University of New MexicoAlbuquerqueNM87131USA

In Software-Defined networks(SDNs),determining how to efficiently achieve Quality of Service(QoS)-aware routing is challenging but critical for significantly improving the performance of a network,where the metrics of QoS can be defined as,for example,average latency,packet loss ratio,and *** SDN controller can use network statistics and a Deep Reinforcement Learning(DRL)method to resolve this *** this paper,we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism(AQROM)to determine routing strategies that balance the traffic loads in the *** can improve the QoS of the network and reduce the training time via dynamic routing strategy updates;that is,the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic *** can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states,and actions with respect to the operations in the *** simulations were conducted using OMNeT++and the results demonstrated that AQROM 1)achieved much faster and stable convergence than the Deep Deterministic Policy Gradient(DDPG)and Advantage Actor-Critic(A2C),2)incurred a lower packet loss ratio and latency than Open Shortest Path First(OSPF),DDPG,and A2C,and 3)resulted in higher and more stable throughput than OSPF,DDPG,and A2C.

关键词： Software-defined networks Asynchronous advantage actor-critic QoS-aware routing optimization mechanism

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Secure network Function Computation: Function-Security

Secure Network Function Computation: Function-Security

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IEEE International Symposium on information Theory

作者： Yang Bai Xuan Guang Raymond W. Yeung School of Mathematical Sciences and LPMC Nankai University Tianjin China Department of Information Engineering Institute of Network Coding The Chinese University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In this paper, we put forward the problem of secure network function computation under the function-security constraint. In this model, a target function, of which the inputs are generated at multiple source nodes as source messages, is required to be computed with zero error at a sink node over a network, while a wiretapper, who can access any one but not more than one wiretap set in a given collection of wiretap sets, is not allowed to obtain any information about a target function of the source messages. The secure computing rate of a secure function-computing network code is the average number of times the target function can be securely computed per use of the network. However, characterizing this secure capacity with this general setup is overwhelmingly difficult. In this paper, we consider this secure model in which the target function and the security function are identical to be a linear function; and the wiretapper can eavesdrop any one edge subset up to a size $r$ , referred to as the security level. We obtain a non-trivial upper bound on the secure computing capacity, which is applicable to arbitrary network topologies and arbitrary security levels. When $r=0$ , the upper bound reduces to the computing capacity without security consideration. By applying the upper bound, we obtain an upper bound on the maximum security level such that the function can be securely computed with a positive rate, and fully characterize the secure computing capacity for a class of secure models. Finally, we prove that our upper bound is always larger than or equal to the upper bound previously obtained by Guang et al. on the secure computing capacity for source-security. This is consistent with the relation between the secure computing capacities for source-security and function-security.

关键词： Upper bound Codes network topology Wireless networks Surveillance Forensics Computational modeling

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Soft-Output Successive Cancellation List Decoding

arXiv

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arXiv 2024年

作者： Yuan, Peihong Duffy, Ken R. Médard, Muriel Massachusetts Institute of Technology Network Coding & Reliable Communications Group United States Northeastern University Engineering Probability Information & Communications Laboratory United States

We introduce an algorithm for approximating the codebook probability that is compatible with all successive cancellation (SC)-based decoding algorithms, including SC list (SCL) decoding. This approximation is based on an auxiliary distribution that mimics the dynamics of decoding algorithms with an SC decoding schedule. Based on this codebook probability and SCL decoding, we introduce soft-output SCL (SO-SCL) to generate both blockwise and bitwise soft-output (SO). Using that blockwise SO, we first establish that, in terms of both block error rate (BLER) and undetected error rate (UER), SO-SCL decoding of dynamic Reed-Muller (RM) codes significantly outperforms the CRC-concatenated polar codes from 5G New Radio under SCL decoding. Moreover, using SO-SCL, the decoding misdetection rate (MDR) can be constrained to not exceed any predefined value, making it suitable for practical systems. Proposed bitwise SO can be readily generated from blockwise SO via a weighted sum of beliefs that includes a term where SO is weighted by the codebook probability, resulting in a soft-input soft-output (SISO) decoder. Simulation results for SO-SCL iterative decoding of product codes and generalized LDPC (GLDPC) codes, along with information-theoretical analysis, demonstrate significant superiority over existing list-max and list-sum approximations. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative decoding

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Cross-Frequency Coupling and Intelligent Neuromodulation

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Cyborg and Bionic Systems 2023年第1期4卷 284-297页

作者： Chien-Hung Yeh Chuting Zhang Wenbin Shi Men-Tzung Lo Gerd Tinkhauser Ashwini Oswal School of Information and Electronics Beijing Institute of TechnologyBeijingChina Department of Biomedical Sciences and Engineering National Central UniversityTaoyuanTaiwan Department of Neurology Bern University Hospital and University of BernBernSwitzerland MRC Brain Network Dynamics Unit University of OxfordOxfordUK

Cross-frequency coupling(CFC)reflects(nonlinear)interactions between signals of different *** from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation,interregional interaction,and disease *** present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling,inferring intrinsic rhythms in a targeted frequency band,and causal *** specifically focus on the literature exploring CFC in the context of cognition/memory tasks,sleep,and neurological disorders,such as Alzheimer's disease,epilepsy,and Parkinson's ***,we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and ***,CFC could support advancing the understanding of the neurophysiology of cognition and motor control,serve as a biomarker for disease symptoms,and leverage the optimization of therapeutic interventions,e.g.,closed-loop brain *** the evident advantages of CFC as an investigative and translational tool in neuroscience,further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.

关键词： epilepsy spite evident

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A Communication-efficient Approach of Bayesian Distributed Federated Learning

A Communication-efficient Approach of Bayesian Distributed F...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Sihua Guo, Huayan Zhu, Xu Yin, Changchuan Lau, Vincent K. N. Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Network Beijing China Hong Kong University of Science and Technology Department of Electronics and Communication Engineering Hong Kong Hkust Shenzhen Research Institute Shenzhen China Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen China

ISBN: (纸本)9798350351255

This paper investigates a fully distributed federated learning (FL) problem, in which each device is restricted to only utilize its local dataset and the information received from its adjacent devices that are defined in a communication graph to update the local model weights for minimizing the global loss function. To incorporate the communication graph constraint into the joint posterior distribution, we exploit the fact that the model weights on each device is a function of its local likelihood and local prior and then, the connectivity between adjacent devices is modeled by a Dirichlet distribution. In this way, the joint distribution can be factorized naturally by a factor graph. Based on the Dirichlet-based factor graph, we propose a novel distributed approximate Bayesian inference algorithm that combines loopy belief propagation (LBP) and variational Bayesian inference (VBI) for distributed FL. Specifically, VBI is used to approximate the non-Gaussian marginal posterior as a Gaussian distribution in local training process and then, the global training process resembles Gaussian LBP where only the mean and variance are passed among adjacent devices. Furthermore, we propose a new damping factor design according to the communication graph topology to mitigate the potential divergence and achieve consensus convergence. Simulation results verify that the proposed solution achieves faster convergence speed with better performance than baselines. © 2024 IEEE.

关键词： Federated learning

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