Computational communication delves into the analysis of digital data,social media interactions,and algorithms that shape communication processes,yet few studies focus on the framework and internal structure of the met...Computational communication delves into the analysis of digital data,social media interactions,and algorithms that shape communication processes,yet few studies focus on the framework and internal structure of the methodological framework related to adaptive topics.This study employs text mining techniques to analyze 9795 publications from international scientific citation databases,and outlines a classification framework to describe the methods used in empirical research.The framework highlights traditional quantitative methods and new computational methods.The former conduct statistical analysis on medium-sized and structured samples,while the latter provides microscopic outlooks with extensive data analysis.Experimental results show the thematic distribution,evolution phases,and subject boundaries of the method categories.This study expands the scope of social computing methodology and provides a wealth of empirical insights.展开更多
With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation(5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored t...With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation(5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored to meet the requirements of ultra-reliable and low latency communications(URLLC) in the maritime communication network(MCN). Mobile edge computing(MEC) can achieve high energy efficiency in MCN at the cost of suffering from high control plane latency and low reliability. In terms of this issue, the mobile edge communications, computing, and caching(MEC3) technology is proposed to sink mobile computing, network control, and storage to the edge of the network. New methods that enable resource-efficient configurations and reduce redundant data transmissions can enable the reliable implementation of computing-intension and latency-sensitive applications. The key technologies of MEC3 to enable URLLC are analyzed and optimized in MCN. The best response-based offloading algorithm(BROA) is adopted to optimize task offloading. The simulation results show that the task latency can be decreased by 26.5’ ms, and the energy consumption in terminal users can be reduced to 66.6%.展开更多
In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating c...In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.展开更多
With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensu...With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information security.In order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G networks.Although the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC.This paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of ISCC.We commence by introducing the background and fundamentals of FL and the ISCC framework.Subsequently,the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed.Finally,design guidelines are provided for the incorporation of FL and ISCC.Overall,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.展开更多
The convergence of computation and communication at network edges plays a significant role in coping with computation-intensive and delay-critical tasks.During the stage of network planning,the resource provisioning p...The convergence of computation and communication at network edges plays a significant role in coping with computation-intensive and delay-critical tasks.During the stage of network planning,the resource provisioning problem for edge nodes has to be investigated to provide prior information for future system configurations.This work focuses on how to quantify the computation capabilities of access points at network edges when provisioning resources of computation and communication in multi-cell wireless networks.The problem is formulated as a discrete and non-convex minimization problem,where practical constraints including delay requirements,the inter-cell interference,and resource allocation strategies are considered.An iterative algorithm is also developed based on decomposition theory and fractional programming to solve this problem.The analysis shows that the necessary computation capability needed for certain delay guarantee depends on resource allocation strategies for delay-critical tasks.For delay-tolerant tasks,it can be approximately estimated by a derived lower bound which ignores the scheduling strategy.The efficiency of the proposed algorithm is demonstrated using numerical results.展开更多
Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent...Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.展开更多
The growing number of mobile users, as well as the diversification in types of services have resulted in increasing demands for wireless network bandwidth in recent years. Although evolving transmission techniques are...The growing number of mobile users, as well as the diversification in types of services have resulted in increasing demands for wireless network bandwidth in recent years. Although evolving transmission techniques are able to enlarge the network capacity to some degree, they still cannot satisfy the requirements of mobile users. Meanwhile, following Moore's Law, the data processing capabilities of mobile user terminals are continuously improving. In this paper, we explore possible methods of trading strong computational power at wireless terminals for transmission efficiency of communications. Taking the specific scenario of wireless video conversation, we propose a model-based video coding scheme by learning the structures in multimedia contents. Benefiting from both strong computing capability and pre-learned model priors, only low-dimensional parameters need to be transmitted; and the intact multimedia contents can also be reconstructed at the receivers in real-time. Experiment results indicate that, compared to conventional video codecs, the proposed scheme significantly reduces the data rate with the aid of computational capability at wireless terminals.展开更多
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 inference.However,due to privacy res...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 inference.However,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 sever.One approach to mitigate these problems is federated learning(FL),which enables the devices to train a common machine learning model without data sharing and transmission.This paper provides a comprehensive overview of FL applications for envisioned sixth generation(6G)wireless networks.In particular,the essential requirements for applying FL to wireless communications are first described.Then potential FL applications in wireless communications are detailed.The main problems and challenges associated with such applications are discussed.Finally,a comprehensive FL implementation for wireless communications is described.展开更多
An entangled coherent state （ECS） is one type of entanglement, which is widely discussed in the application of quan- tum information processing （QIP）. In this paper, we propose an entanglement concentration protoc...An entangled coherent state （ECS） is one type of entanglement, which is widely discussed in the application of quan- tum information processing （QIP）. In this paper, we propose an entanglement concentration protocol （ECP） to distill the maximally entangled W-type ECS from the partially entangled W-type ECS. In the ECP, we adopt the balanced beam split- ter （BS） to make the parity check measurement. Our ECP is quite different from the conventional ECPs. After performing the ECP, not only can we obtain the maximally entangled ECS with some success probability, but also we can increase the amplitude of the coherent state. Therefore, it is especially useful in long-distance quantum communication, if the photon loss is considered.展开更多
Hybrid entangled state （HES） is a new type of entanglement, which combines the advantages of an entangled po- larization state and an entangled coherent state. HES is widely discussed in the applications of quantum ...Hybrid entangled state （HES） is a new type of entanglement, which combines the advantages of an entangled po- larization state and an entangled coherent state. HES is widely discussed in the applications of quantum communication and computation. In this paper, we propose three entanglement concentration protocols （ECPs） for Bell-type HES, W-type HES, and cluster-type HES, respectively. After performing these ECPs, we can obtain the maximally entangled HES with some success probability. All the ECPs exploit the single coherent state to complete the concentration. These protocols are based on the linear optics, which are feasible in future experiments.展开更多
High-speed large-bandwidth networks and growth in rich internet applications has brought unprecedented pressure to bear on telecom operators. Consequently, operators need to play to the advantages of their networks, m...High-speed large-bandwidth networks and growth in rich internet applications has brought unprecedented pressure to bear on telecom operators. Consequently, operators need to play to the advantages of their networks, make good use of their large customer bases, and expand their business resources in service, platform, and interface. Network and customer resources should be integrated in order to create new business ecosystems. This paper describes new threats and challenges facing telecom operators and analyzes how leading operators are handling transformation in terms of operations and business model. A new concept called distributed intelligent open system （DIOS）—a public computing communication network—is proposed. The architecture and key technologies of DIOS is discussed in detail.展开更多
This paper studies a federated edge learning system,in which an edge server coordinates a set of edge devices to train a shared machine learning(ML)model based on their locally distributed data samples.During the dist...This paper studies a federated edge learning system,in which an edge server coordinates a set of edge devices to train a shared machine learning(ML)model based on their locally distributed data samples.During the distributed training,we exploit the joint communication and computation design for improving the system energy efficiency,in which both the communication resource allocation for global ML-parameters aggregation and the computation resource allocation for locally updating ML-parameters are jointly optimized.In particular,we consider two transmission protocols for edge devices to upload ML-parameters to edge server,based on the non-orthogonal multiple access(NOMA)and time division multiple access(TDMA),respectively.Under both protocols,we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy,by jointly optimizing the transmission power and rates at edge devices for uploading ML-parameters and their central processing unit(CPU)frequencies for local update.We propose efficient algorithms to solve the formulated energy minimization problems by using the techniques from convex optimization.Numerical results show that as compared to other benchmark schemes,our proposed joint communication and computation design significantly can improve the energy efficiency of the federated edge learning system,by properly balancing the energy tradeoff between communication and computation.展开更多
Tsinghua Science and Technology was started publication in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scie...Tsinghua Science and Technology was started publication in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, electronic engineering, and other IT fields. It is indexed by EI and other abstracting indexes. From 2012, the journal enters into IEEE Xplore Digital Library and all papers published there are freely downloadable.展开更多
Distributed secure quantum machine learning （DSQML） enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. More...Distributed secure quantum machine learning （DSQML） enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. Moreover, DSQML can be extended to a more general case that the client does not have enough data, and resorts both the remote quantum server and remote databases to perform the secure machi~ learning. Here we propose a DSQML protocol that the client can classify two-dimensional vectors to dif- ferent clusters, resorting to a remote small-scale photon quantum computation processor. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for future ＂big data＂.展开更多
Internet of Vehicles(IoV)is a distributed network of connected cars,roadside infrastructure,wireless communication networks,and central cloud platforms.Wireless recommendations play an important role in the IoV networ...Internet of Vehicles(IoV)is a distributed network of connected cars,roadside infrastructure,wireless communication networks,and central cloud platforms.Wireless recommendations play an important role in the IoV network,for example,recommending appropriate routes,recommending driving strategies,and recommending content.In this paper,we review some of the key techniques in recommendations and discuss what are the opportunities and challenges to deploy these wireless recommendations in the IoV.展开更多
In this paper, we present a novel, dynamic collaboration cloud platform in which a Combinatorial Auction（CA）-based market model enables the platform to run effectively. The platform can facilitate expense reduction ...In this paper, we present a novel, dynamic collaboration cloud platform in which a Combinatorial Auction（CA）-based market model enables the platform to run effectively. The platform can facilitate expense reduction and improve the scalability of the cloud, which is divided into three layers： The user-layer receives requests from end-users, the auction-layer matches the requests with the cloud services provided by the Cloud Service Provider（CSP）, and the CSP-layer forms a coalition to improve serving ability to satisfy complex requirements of users.In fact, the aim of the coalition formation is to find suitable partners for a particular CSP. However, identifying a suitable combination of partners to form the coalition is an NP-hard problem. Hence, we propose approximation algorithms for the coalition formation. The Breadth Traversal Algorithm（BTA） and Revised Ant Colony Algorithm（RACA） are proposed to form a coalition when bidding for a single cloud service in the auction. The experimental results show that RACA outperforms the BTA in bid price. Other experiments were conducted to evaluate the impact of the communication cost on coalition formation and to assess the impact of iteration times for the optimal bidding price. In addition, the performance of the market model was compared to the existing CA-based model in terms of economic efficiency.展开更多
基金supported by the National Social Science Foundation of China(No.23&ZD215)National Natural Science Foundation of China(No.62206112)Key Laboratory of Smart Education of Guangdong Higher Education Institutes,Jinan University(No.2022LSYS003).
文摘Computational communication delves into the analysis of digital data,social media interactions,and algorithms that shape communication processes,yet few studies focus on the framework and internal structure of the methodological framework related to adaptive topics.This study employs text mining techniques to analyze 9795 publications from international scientific citation databases,and outlines a classification framework to describe the methods used in empirical research.The framework highlights traditional quantitative methods and new computational methods.The former conduct statistical analysis on medium-sized and structured samples,while the latter provides microscopic outlooks with extensive data analysis.Experimental results show the thematic distribution,evolution phases,and subject boundaries of the method categories.This study expands the scope of social computing methodology and provides a wealth of empirical insights.
基金the National S&T Major Project (No. 2018ZX03001011)the National Key R&D Program(No.2018YFB1801102)+1 种基金the National Natural Science Foundation of China (No. 61671072)the Beijing Natural Science Foundation (No. L192025)
文摘With the increasing maritime activities and the rapidly developing maritime economy, the fifth-generation(5G) mobile communication system is expected to be deployed at the ocean. New technologies need to be explored to meet the requirements of ultra-reliable and low latency communications(URLLC) in the maritime communication network(MCN). Mobile edge computing(MEC) can achieve high energy efficiency in MCN at the cost of suffering from high control plane latency and low reliability. In terms of this issue, the mobile edge communications, computing, and caching(MEC3) technology is proposed to sink mobile computing, network control, and storage to the edge of the network. New methods that enable resource-efficient configurations and reduce redundant data transmissions can enable the reliable implementation of computing-intension and latency-sensitive applications. The key technologies of MEC3 to enable URLLC are analyzed and optimized in MCN. The best response-based offloading algorithm(BROA) is adopted to optimize task offloading. The simulation results show that the task latency can be decreased by 26.5’ ms, and the energy consumption in terminal users can be reduced to 66.6%.
基金supported in part by National Key R&D Program of China(2019YFE0196400)Key Research and Development Program of Shaanxi(2022KWZ09)+4 种基金National Natural Science Foundation of China(61771358,61901317,62071352)Fundamental Research Funds for the Central Universities(JB190104)Joint Education Project between China and Central-Eastern European Countries(202005)the 111 Project(B08038)。
文摘In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.
文摘With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information security.In order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G networks.Although the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC.This paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of ISCC.We commence by introducing the background and fundamentals of FL and the ISCC framework.Subsequently,the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed.Finally,design guidelines are provided for the incorporation of FL and ISCC.Overall,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.
基金Supported by the Shanghai Sailing Program(No.18YF1427900)the National Natural Science Foundation of China(No.61471347)the Shanghai Pujiang Program(No.2020PJD081).
文摘The convergence of computation and communication at network edges plays a significant role in coping with computation-intensive and delay-critical tasks.During the stage of network planning,the resource provisioning problem for edge nodes has to be investigated to provide prior information for future system configurations.This work focuses on how to quantify the computation capabilities of access points at network edges when provisioning resources of computation and communication in multi-cell wireless networks.The problem is formulated as a discrete and non-convex minimization problem,where practical constraints including delay requirements,the inter-cell interference,and resource allocation strategies are considered.An iterative algorithm is also developed based on decomposition theory and fractional programming to solve this problem.The analysis shows that the necessary computation capability needed for certain delay guarantee depends on resource allocation strategies for delay-critical tasks.For delay-tolerant tasks,it can be approximately estimated by a derived lower bound which ignores the scheduling strategy.The efficiency of the proposed algorithm is demonstrated using numerical results.
基金supported by National Natural Science Foundation of China(62101088,61801076,61971336)Natural Science Foundation of Liaoning Province(2022-MS-157,2023-MS-108)+1 种基金Key Laboratory of Big Data Intelligent Computing Funds for Chongqing University of Posts and Telecommunications(BDIC-2023-A-003)Fundamental Research Funds for the Central Universities(3132022230).
文摘Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.
基金supported by the National Basic Research Project of China (973) (2013CB329006)National Natural Science Foundation of China (NSFC, 61101071,61471220, 61021001)Tsinghua University Initiative Scientific Research Program
文摘The growing number of mobile users, as well as the diversification in types of services have resulted in increasing demands for wireless network bandwidth in recent years. Although evolving transmission techniques are able to enlarge the network capacity to some degree, they still cannot satisfy the requirements of mobile users. Meanwhile, following Moore's Law, the data processing capabilities of mobile user terminals are continuously improving. In this paper, we explore possible methods of trading strong computational power at wireless terminals for transmission efficiency of communications. Taking the specific scenario of wireless video conversation, we propose a model-based video coding scheme by learning the structures in multimedia contents. Benefiting from both strong computing capability and pre-learned model priors, only low-dimensional parameters need to be transmitted; and the intact multimedia contents can also be reconstructed at the receivers in real-time. Experiment results indicate that, compared to conventional video codecs, the proposed scheme significantly reduces the data rate with the aid of computational capability at wireless terminals.
基金This work was supported by research grants from the Engineering and Physical Sciences Research Council(EPSRC),UK(EP/T015985/1)from US National Science Foundation(CCF-1908308).
文摘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 inference.However,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 sever.One approach to mitigate these problems is federated learning(FL),which enables the devices to train a common machine learning model without data sharing and transmission.This paper provides a comprehensive overview of FL applications for envisioned sixth generation(6G)wireless networks.In particular,the essential requirements for applying FL to wireless communications are first described.Then potential FL applications in wireless communications are detailed.The main problems and challenges associated with such applications are discussed.Finally,a comprehensive FL implementation for wireless communications is described.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11347110,11104159,and 61201164)the Qing Lan Project,Jiangsu Province,1311 Talent Plan,Nanjing University of Posts and Telecommunicationsthe Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘An entangled coherent state （ECS） is one type of entanglement, which is widely discussed in the application of quan- tum information processing （QIP）. In this paper, we propose an entanglement concentration protocol （ECP） to distill the maximally entangled W-type ECS from the partially entangled W-type ECS. In the ECP, we adopt the balanced beam split- ter （BS） to make the parity check measurement. Our ECP is quite different from the conventional ECPs. After performing the ECP, not only can we obtain the maximally entangled ECS with some success probability, but also we can increase the amplitude of the coherent state. Therefore, it is especially useful in long-distance quantum communication, if the photon loss is considered.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11474168 and 61401222)the Natural Science Foundation of Jiangsu Province+3 种基金China(Grant No.BK20151502)the Qing Lan Project in Jiangsu Province,Chinathe Natural Science Foundation of Jiangsu Higher Education Institutions,China(Grant No.15KJA120002)the Priority Academic Development Program of Jiangsu Higher Education Institutions,China
文摘Hybrid entangled state （HES） is a new type of entanglement, which combines the advantages of an entangled po- larization state and an entangled coherent state. HES is widely discussed in the applications of quantum communication and computation. In this paper, we propose three entanglement concentration protocols （ECPs） for Bell-type HES, W-type HES, and cluster-type HES, respectively. After performing these ECPs, we can obtain the maximally entangled HES with some success probability. All the ECPs exploit the single coherent state to complete the concentration. These protocols are based on the linear optics, which are feasible in future experiments.
文摘High-speed large-bandwidth networks and growth in rich internet applications has brought unprecedented pressure to bear on telecom operators. Consequently, operators need to play to the advantages of their networks, make good use of their large customer bases, and expand their business resources in service, platform, and interface. Network and customer resources should be integrated in order to create new business ecosystems. This paper describes new threats and challenges facing telecom operators and analyzes how leading operators are handling transformation in terms of operations and business model. A new concept called distributed intelligent open system （DIOS）—a public computing communication network—is proposed. The architecture and key technologies of DIOS is discussed in detail.
基金the National Key R&D Program of China under Grant 2018YFB1800800Guangdong Province Key Area R&D Program under Grant 2018B030338001the Natural Science Foundation of China under Grant U2001208。
文摘This paper studies a federated edge learning system,in which an edge server coordinates a set of edge devices to train a shared machine learning(ML)model based on their locally distributed data samples.During the distributed training,we exploit the joint communication and computation design for improving the system energy efficiency,in which both the communication resource allocation for global ML-parameters aggregation and the computation resource allocation for locally updating ML-parameters are jointly optimized.In particular,we consider two transmission protocols for edge devices to upload ML-parameters to edge server,based on the non-orthogonal multiple access(NOMA)and time division multiple access(TDMA),respectively.Under both protocols,we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy,by jointly optimizing the transmission power and rates at edge devices for uploading ML-parameters and their central processing unit(CPU)frequencies for local update.We propose efficient algorithms to solve the formulated energy minimization problems by using the techniques from convex optimization.Numerical results show that as compared to other benchmark schemes,our proposed joint communication and computation design significantly can improve the energy efficiency of the federated edge learning system,by properly balancing the energy tradeoff between communication and computation.
文摘Tsinghua Science and Technology was started publication in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, electronic engineering, and other IT fields. It is indexed by EI and other abstracting indexes. From 2012, the journal enters into IEEE Xplore Digital Library and all papers published there are freely downloadable.
基金supported by the National Natural Science Foundation of China(11474168 and 61401222)the Natural Science Foundation of Jiangsu Province(BK20151502)+1 种基金the Qing Lan Project in Jiangsu Provincea Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Distributed secure quantum machine learning （DSQML） enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. Moreover, DSQML can be extended to a more general case that the client does not have enough data, and resorts both the remote quantum server and remote databases to perform the secure machi~ learning. Here we propose a DSQML protocol that the client can classify two-dimensional vectors to dif- ferent clusters, resorting to a remote small-scale photon quantum computation processor. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for future ＂big data＂.
基金This work was supported in part by the National Natural Science Foundation of China(NSFC)(Nos.61901534 and 61702205)the Guangdong Basic and Applied Basic Research Foundation(No.2019B1515120032)+1 种基金the Science,Technology and Innovation Commission of Shenzhen Municipality(No.JCYJ20190807155617099)the Hong Kong RGC ECS(No.21212419).
文摘Internet of Vehicles(IoV)is a distributed network of connected cars,roadside infrastructure,wireless communication networks,and central cloud platforms.Wireless recommendations play an important role in the IoV network,for example,recommending appropriate routes,recommending driving strategies,and recommending content.In this paper,we review some of the key techniques in recommendations and discuss what are the opportunities and challenges to deploy these wireless recommendations in the IoV.
基金supported by the National Natural Science Foundation of China (Nos. 61070133, 61170201, and 61472344)the Collegiate Natural Science Foundation of Jiangsu Province (Grant No. 11KJD520011)+1 种基金Six talent peaks project in Jiangsu Province (No. 2011-DZXX-032)the Scientific Research Foundation of Graduate School of Jiangsu Province (No. CXZZ13 0901)
文摘In this paper, we present a novel, dynamic collaboration cloud platform in which a Combinatorial Auction（CA）-based market model enables the platform to run effectively. The platform can facilitate expense reduction and improve the scalability of the cloud, which is divided into three layers： The user-layer receives requests from end-users, the auction-layer matches the requests with the cloud services provided by the Cloud Service Provider（CSP）, and the CSP-layer forms a coalition to improve serving ability to satisfy complex requirements of users.In fact, the aim of the coalition formation is to find suitable partners for a particular CSP. However, identifying a suitable combination of partners to form the coalition is an NP-hard problem. Hence, we propose approximation algorithms for the coalition formation. The Breadth Traversal Algorithm（BTA） and Revised Ant Colony Algorithm（RACA） are proposed to form a coalition when bidding for a single cloud service in the auction. The experimental results show that RACA outperforms the BTA in bid price. Other experiments were conducted to evaluate the impact of the communication cost on coalition formation and to assess the impact of iteration times for the optimal bidding price. In addition, the performance of the market model was compared to the existing CA-based model in terms of economic efficiency.
