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Transmission Energy Allocation for Over-the-Air Computation with Energy Harvesting

Journal of Communications and Information Networks 2024年第2期9卷 126-136页

作者： Siyao Zhang Zixiang Ren Xinmin Li Yin Long Jie Xu shuguang cui School of Computer Science and Technology Southwest University of Science and TechnologyMianyang 621010China Shenzhen Future Network of Intelligence Institute(FNii-Shenzhen) The Chinese University of Hong KongShenzhenGuangdong 518172China School of Information Science and Technology University of Science and Technology of ChinaHefei 230027China FNii-Shenzhen The Chinese University of Hong KongShenzhenGuangdong 518172China College of Computer Science Chengdu UniversityChengdu 610100China School of Science and Engineering FNii-Shenzhenand Guangdong Provincial Key Laboratory of Future Networks of IntelligenceThe Chinese University of Hong KongShenzhenGuangdong 518172China

Over-the-air computation(AirComp)has recently emerged as a promising multiple-access technique for fast wireless data aggregation(WDA)from distributed wireless devices(WDs). This paper investigates an energy harvesting (EH) AirComp system, in which multiple EH-powered single-antenna WDs simultaneously send wireless signals to a single-antenna access point (AP) with conventional energy supply for WDA via AirComp. Under this setup, we minimize the average computation mean square error(MSE)over a particular time period, by jointly optimizing the transmit energy allocation at the WDs and the AirComp denoising factors at the AP over time, subject to the energy causality constraints at individual WDs. First, we consider the offline scenario by assuming that the energy state information(ESI)and channel state information (CSI) are non-causally known at the beginning of the period, in which the formulated average MSE minimization corresponds to a non-convex optimization problem. We present a high-quality converged solution by using the techniques of alternating optimization and convex optimization. It is shown that for each WD,if the EH rate is sufficiently high,then the channel inversion power allocation is adopted;while if the EH rate is low, then all the harvested energy should be used up for transmission with proper energy allocation over time. Next, we consider the online scenario with causal ESI and CSI,in which the MSE minimization becomes a stochastic optimization *** this scenario, we present an offline-inspired online algorithm to obtain efficient online energy allocation designs by utilizing the obtained offline solutions. Finally,numerical results show that the proposed designs significantly outperform two benchmark schemes with power-halving and full-power transmission,respectively.

关键词： over-the-air computation(AirComp) power allocation energy harvesting(EH) optimization

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智能反射面辅助通信中的信道估计方法

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无线电通信技术 2024年第2期50卷 238-244页

作者：王兆瑞刘亮崔曙光香港中文大学(深圳)未来智联网络研究院广东深圳518172 香港中文大学(深圳)理工学院广东深圳518172 香港理工大学电子与信息工程系中国香港999077

信道信息对于智能反射面(Intelligent Reflecting Surface, IRS)辅助的通信系统十分关键。由于IRS反射单元的数量十分巨大,信道估计和信道反馈的开销一直制约着IRS辅助的通信系统的性能。为解决这一难题,介绍了IRS辅助的通信系统中信道... 详细信息

信道信息对于智能反射面(Intelligent Reflecting Surface, IRS)辅助的通信系统十分关键。由于IRS反射单元的数量十分巨大,信道估计和信道反馈的开销一直制约着IRS辅助的通信系统的性能。为解决这一难题,介绍了IRS辅助的通信系统中信道的特性。不同用户共享同一基站(Base Station, BS)-IRS信道,而BS-IRS-用户串联信道具有很强的关联性。基于这一特有的信道特性,提出了IRS辅助的通信系统上行信道估计方法、下行信道估计和信道反馈方法;理论性地刻画了以上方法所需要的最小信道估计开销和信道反馈开销,以揭示所提方法在IRS辅助的通信系统中相对于传统信道信息获取方法的巨大优势。

关键词：智能反射面信道估计信道反馈

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Thermally removable in-situ formed ZnO template for synthesis of hierarchically porous N-doped carbon nanofibers for enhanced electrocatalysis

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Nano Research 2016年第8期9卷 2270-2283页

作者： shuguang Wang Zhentao cui Jinwen Qin Minhua Cao Key Laboratory of Cluster Science Ministry of Education of China Beijing Key Laboratory of Photoelectronic/Electrophotonic ConversionMaterials Department of Chemistry Beijing Institute of Technology Beijing 100081 China

Rational design and simple synthesis of one-dimensional nanofibers with high specific surface areas and hierarchically porous structures are still challenging. In the present work, a novel strategy utilizing a thermally removable template was developed to synthesize hierarchically porous N-doped carbon nanofibers （HP-NCNFs） through the use of simple electrospinning technology coupled with subsequent pyrolysis. During the pyrolysis process, ZnO nanoparticles can be formed in situ and act as a thermally removable template due to their decomposition and sublimation under high-temperature conditions. The resulting HP-NCNFs have lengths of up to hundreds of micrometers with an average diameter of 300 nm and possess a hierarchically porous structure throughout. Such unique structures endow HP-NCNFs with a high specific surface area of up to 829.5 m2-g 1, which is 2.6 times higher than that （323.2 m2.g 1） of conventional N-doped carbon nanofibers （NCNFs）. Compared with conventional NCNFs, the HP-NCNF catalyst exhibited greatly enhanced catalytic performance and improved kinetics for the oxygen reduction reaction （ORR） in alkaline media. Moreover, the HP-NCNFs even showed better stability and stronger methanol crossover effect tolerance than the commerdal Pt-C catalyst. The optimized ORR performance can be attributed to the synergetic contribution of continuous and three-dimensional （3D） cross-linked structures, graphene-like structure on the edge of the HP- NCNFs, high specific surface area, and a hierarchically porous structure.

关键词： zinc oxide hierarchically porousstructure thermally removable formed in situ oxygen reduction reaction

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SMART GRID COMMUNICATIONS

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中国通信：英文版 2015年第3期12卷 I0002-I0003页

作者： XIE Le cui shuguang Electric Power and Power Electronics Group at Texas A&M University Texas A&M University

As the society increasingly emphasizes the need of clean and renewable energy systems,the electric power industry is undergoing profound changes to transform a passive,hierarchical grid into an active and open-access smart *** by advances in sensing,communication,and actuation,future smart grids offer much broader opportunities for cross-fertilization between the traditional power engineering community and the communication *** special issue pres-

关键词：电网通信智能能源系统电力行业电力工程测试压力可再生社区

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Air interface design for edge intelligence

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The Journal of China Universities of Posts and Telecommunications 2022年第1期29卷 13-26,124页

作者： Zhang Zezhong Chen Mingzhe Xu Jie cui shuguang Future Network of Intelligence Institute(FNii) The Chinese University of Hong Kong(Shenzhen)Shenzhen 518172China Department of Electrical Engineering Princeton UniversityPrincetonNJ 08544USA

Recent breakthroughs in artificial intelligence(AI) give rise to a plethora of intelligent applications and services based on machine learning algorithms such as deep neural networks(DNNs). With the proliferation of Internet of things(IoT) and mobile edge computing, these applications are being pushed to the network edge, thus enabling a new paradigm termed as edge intelligence. This provokes the demand for decentralized implementation of learning algorithms over edge networks to distill the intelligence from distributed data, and also calls for new communication-efficient designs in air interfaces to improve the privacy by avoiding raw data exchange. This paper provides a comprehensive overview on edge intelligence, by particularly focusing on two paradigms named edge learning and edge inference, as well as the corresponding communication-efficient solutions for their implementations in wireless systems. Several insightful theoretical results and design guidelines are also provided.

关键词： artificial intelligence air interface edge intelligence edge inference federated learning

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Federated Learning for 6G:Applications,Challenges,and Opportunities

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Engineering 2022年第1期8卷 33-41页

作者： Zhaohui Yang Mingzhe Chen Kai-Kit Wong H.Vincent Poor shuguang cui Department of Electronic and Electrical Engineering University College LondonLondon WC1E 6BTUK Department of Electrical and Computer Engineering Princeton UniversityPrincetonNJ 08544USA Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhen 518172China School of Science and Engineering and Future Network of Intelligence Institute The Chinese University of Hong KongShenzhen 518172China

Standard machine-learning approaches involve the centralization of training data in a data center,where centralized machine-learning algorithms can be applied for data analysis and ***,due to privacy restrictions and limited communication resources in wireless networks,it is often undesirable or impractical for the devices to transmit data to parameter *** approach to mitigate these problems is federated learning(FL),which enables the devices to train a common machine learning model without data sharing and *** paper provides a comprehensive overview of FL applications for envisioned sixth generation(6G)wireless *** particular,the essential requirements for applying FL to wireless communications are first *** potential FL applications in wireless communications are *** main problems and challenges associated with such applications are ***,a comprehensive FL implementation for wireless communications is described.

关键词： Federated learning 6G Reconfigurable intelligent surface Semantic communication Sensing Communication and computing

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Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey

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China Communications 2022年第1期19卷 218-237页

作者： Kai Chen Qinglei Kong Yijue Dai Yue Xu Feng Yin Lexi Xu shuguang cui School of Science and Engineering The Chinese University of Hong KongShenzhen 518172China Future Network of Intelligence Institute(FNii) The Chinese University of Hong KongShenzhen 518172China School of Information Science and Technology University of Science and Technology of ChinaHefei 230026China Research Institute China United Network Communications CorporationBeijing 100048China Alibaba Group Hangzhou 310052China

Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning techniques,next-generation data-driven communication systems will be intelligent with unique characteristics of expressiveness, scalability, interpretability, and uncertainty awareness, which can confidently involve diversified latent demands and personalized services in the foreseeable future. In this paper, we review a promising family of nonparametric Bayesian machine learning models,i.e., Gaussian processes(GPs), and their applications in wireless communication. Since GP models demonstrate outstanding expressive and interpretable learning ability with uncertainty, they are particularly suitable for wireless communication. Moreover, they provide a natural framework for collaborating data and empirical models(DEM). Specifically, we first envision three-level motivations of data-driven wireless communication using GP models. Then, we present the background of the GPs in terms of covariance structure and model inference. The expressiveness of the GP model using various interpretable kernels, including stationary, non-stationary, deep and multi-task kernels,is showcased. Furthermore, we review the distributed GP models with promising scalability, which is suitable for applications in wireless networks with a large number of distributed edge devices. Finally, we list representative solutions and promising techniques that adopt GP models in various wireless communication applications.

关键词： wireless communication Gaussian process machine learning kernel interpretability uncertainty

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SELECTED PAPERS FROM IEEE/CIC ICCC 2018

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China Communications 2019年第9期16卷 15-17页

作者： Zhiyong Feng shuguang cui T. Aaron Gulliver Zhiqing Wei BUPT Beijing Peoples R China Stanford Univ Elect Engn Stanford CA 94305 USA Univ Victoria Elect Engn Victoria BC Canada Carleton Univ Acad Appointments Ottawa ON Canada Univ Canterbury Christchurch New Zealand

IEEE/CIC International Conference on Communication in China(ICCC)is one of the flagship conferences in Asian-Pacific region held by IEEE Communication Society(ComSoc)and China Institute of Communication(CIC).IEEE/CIC ICCC 2018 was held in Beijing,China,on August 16-18,*** key theme is“Ubiquitous Network,Intelligence and Society”,which broadly covers the theoretical and practical innovations in the area of *** technical program of IEEE/CIC ICCC 2018 features 6 world-class keynote speeches which are presented by Weihua Zhuang(Fellow of IEEE,Royal Society of Canada,Canadian Academy of Engineering,and Engineering Institute of Canada),Abbas Jamalipour(Fellow of IEEE,IEICE,and Institution of Engineers Australia),Tao Zhang,Ying-Chang Liang(IEEE Fellow),James Kimery,and Sumei Sun(IEEE Fellow).

关键词： Canada Intelligence Institution

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AI-EMPOWERED MILLIMETER WAVE COMMUNICATION AND NETWORKING

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China Communications 2020年第10期17卷 97-99页

作者： Wenjun Xu Chuan Huang Yongming Huang Chau Yuen shuguang cui Beijing Univ Posts & Telecommun BUPT Sch Artificial Intelligence Beijing Peoples R China Minist Educ Key Lab Universal Wireless Commun Beijing Peoples R China Arizona State Univ Tempe AZ USA Princeton Univ Princeton NJ 08544 USA Chinese Univ Hong Kong Shenzhen Hong Kong Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing Peoples R China Lucent Technol Bell Labs Murray Hill NJ USA Inst Infocomm Res I2R Singapore Singapore Singapore Univ Technol & Design Singapore Singapore Univ Arizona Elect & Comp Engn Tucson AZ 85721 USA Texas A&M Univ College Stn TX 77843 USA Univ Calif Davis Davis CA USA CUHK Shenzhen Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China

Thanks to the abundant and attractive spectrum resource in the millimeter-wave(mmWave)band,mmWave technologies have been widely applied to the fifth-generation(5G)mobile communication system and will continuously evolve towards the future beyond 5G systems as ***,achieving the pervasive application of mmWave for 5G and beyond still faces many challenges,for example,the severe path-loss and vulnerability in blockage of mmWave propagation may have a great impact on mmWave communication and networking.

关键词： beyond communication millimeter

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU shuguang cui Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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