Spectrum management and resource allocation(RA)problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks.The traditional approaches for solving such...Spectrum management and resource allocation(RA)problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks.The traditional approaches for solving such problems usually consume time and memory,especially for large-size problems.Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems,especially the generative model of the deep neural networks.In this work,we propose a resource allocation deep autoencoder network,as one of the promising generative models,for enabling spectrum sharing in underlay device-to-device(D2D)communication by solving linear sum assignment problems(LSAPs).Specifically,we investigate the performance of three different architectures for the conditional variational autoencoders(CVAE).The three proposed architecture are the convolutional neural network(CVAECNN)autoencoder,the feed-forward neural network(CVAE-FNN)autoencoder,and the hybrid(H-CVAE)autoencoder.The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques,such as the Hungarian algorithm,due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time.Moreover,the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques.展开更多
Reliable identity management and authentication are significant for network security.In recent years,as traditional centralized identity management systems suffer from security and scalability problems,decentralized i...Reliable identity management and authentication are significant for network security.In recent years,as traditional centralized identity management systems suffer from security and scalability problems,decentralized identity management has received considerable attention in academia and industry.However,with the increasing sharing interaction among each domain,management and authentication of decentralized identity has raised higher requirements for cross-domain trust and faced implementation challenges galore.To solve these problems,we propose BIdM,a decentralized crossdomain identity management system based on blockchain.We design a decentralized identifier(DID)for naming identities based on the consortium blockchain technique.Since the identity subject fully controls the life cycle and ownership of the proposed DID,it can be signed and issued without a central authentication node’s intervention.Simultaneously,every node in the system can participate in identity authentication and trust establishment,thereby solving the centralized mechanism’s single point of failure problem.To further improve authentication efficiency and protect users’privacy,BIdM introduces a one-way accumulator as an identity data structure,which guarantees the validity of entity identity.We theoretically analyze the feasibility and performance of BIdM and conduct evaluations on a prototype implementation.The experimental results demonstrate that BIdM achieves excellent optimization on cross-domain authentication compared with existing identity management systems.展开更多
基金supported in part by the China NSFC Grant 61872248Guangdong NSF 2017A030312008+1 种基金Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China (Grant No.161064)GDUPS (2015)
文摘Spectrum management and resource allocation(RA)problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks.The traditional approaches for solving such problems usually consume time and memory,especially for large-size problems.Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems,especially the generative model of the deep neural networks.In this work,we propose a resource allocation deep autoencoder network,as one of the promising generative models,for enabling spectrum sharing in underlay device-to-device(D2D)communication by solving linear sum assignment problems(LSAPs).Specifically,we investigate the performance of three different architectures for the conditional variational autoencoders(CVAE).The three proposed architecture are the convolutional neural network(CVAECNN)autoencoder,the feed-forward neural network(CVAE-FNN)autoencoder,and the hybrid(H-CVAE)autoencoder.The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques,such as the Hungarian algorithm,due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time.Moreover,the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques.
基金Key-Area Research and Development Program of Guangdong Province(2020B0101090003)National Natural Science Foundation of China(62072012)+2 种基金Shenzhen Research Project(JSGG20191129110603831)Shenzhen Key Laboratory Project(ZDSYS201802051831427)the project PCL Future Regional Network Facilities for Large Scale Experiments and Applications。
文摘Reliable identity management and authentication are significant for network security.In recent years,as traditional centralized identity management systems suffer from security and scalability problems,decentralized identity management has received considerable attention in academia and industry.However,with the increasing sharing interaction among each domain,management and authentication of decentralized identity has raised higher requirements for cross-domain trust and faced implementation challenges galore.To solve these problems,we propose BIdM,a decentralized crossdomain identity management system based on blockchain.We design a decentralized identifier(DID)for naming identities based on the consortium blockchain technique.Since the identity subject fully controls the life cycle and ownership of the proposed DID,it can be signed and issued without a central authentication node’s intervention.Simultaneously,every node in the system can participate in identity authentication and trust establishment,thereby solving the centralized mechanism’s single point of failure problem.To further improve authentication efficiency and protect users’privacy,BIdM introduces a one-way accumulator as an identity data structure,which guarantees the validity of entity identity.We theoretically analyze the feasibility and performance of BIdM and conduct evaluations on a prototype implementation.The experimental results demonstrate that BIdM achieves excellent optimization on cross-domain authentication compared with existing identity management systems.
