Structural redundancy elimination in case resource pools （CRP） is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this pape...Structural redundancy elimination in case resource pools （CRP） is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this paper proposes a novel approach to ensure redundancy elimination of a reasoning system in CRP. By using α entropy and mutual information, functional measures to eliminate redundancy of a system are developed with respect to a set of outputs. These measures help to distinguish both the optimal feature and the relations among the nodes in reasoning networks from the redundant ones with the elimination criterion. Based on the optimal feature and its harmonic weight, a model for knowledge reasoning in CRP （CRPKR） is built to complete the task of query matching, and the missing values are estimated with Bayesian networks. Moreover, the robustness of decisions is verified through parameter analyses. This approach is validated by the simulation with benchmark data sets using cloud SQL. Compared with several state-of-the-art techniques, the results show that the proposed approach has a good performance and boosts the robustness of decisions.展开更多
As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network.Since e...As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network.Since electromagnetic wave propagation is affected by the environment,constructing the relationship between the environment and radio wave propagation is the key to implementing DTC.In the existing methods,the environmental information inputted into the neural network has many dimensions,and the correlation between the environment and the channel is unclear,resulting in a highly complex relationship construction process.To solve this issue,we propose a unified construction method of radio environment knowledge(REK)inspired by the electromagnetic wave property to quantify the propagation contribution based on easily obtainable location information.An effective scatterer determination scheme based on random geometry is proposed which reduces redundancy by 90%,87%,and 81%in scenarios with complete openness,impending blockage,and complete blockage,respectively.We also conduct a path loss prediction task based on a lightweight convolutional neural network(CNN)employing a simple two-layer convolutional structure to validate REK’s effectiveness.The results show that only 4 ms of testing time is needed with a prediction error of 0.3,effectively reducing the network complexity.展开更多
基金supported by the National Natural Science Foundation of China (7117114371201087)+1 种基金the Tianjin Municipal Research Program of Application Foundation and Advanced Technology of China (10JCY-BJC07300)the Science and Technology Program of FOXCONN Group (120024001156)
文摘Structural redundancy elimination in case resource pools （CRP） is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this paper proposes a novel approach to ensure redundancy elimination of a reasoning system in CRP. By using α entropy and mutual information, functional measures to eliminate redundancy of a system are developed with respect to a set of outputs. These measures help to distinguish both the optimal feature and the relations among the nodes in reasoning networks from the redundant ones with the elimination criterion. Based on the optimal feature and its harmonic weight, a model for knowledge reasoning in CRP （CRPKR） is built to complete the task of query matching, and the missing values are estimated with Bayesian networks. Moreover, the robustness of decisions is verified through parameter analyses. This approach is validated by the simulation with benchmark data sets using cloud SQL. Compared with several state-of-the-art techniques, the results show that the proposed approach has a good performance and boosts the robustness of decisions.
基金supported by the National Key R&D Program of China(No.2023YFB2904803)the National Natural Science Foundation of China(Nos.62341128,62201087,and 62101069)+2 种基金the National Science Fund for Distinguished Young Scholars,China(No.61925102)the Beijing Natural Science Foundation,China(No.L243002)the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless network.Since electromagnetic wave propagation is affected by the environment,constructing the relationship between the environment and radio wave propagation is the key to implementing DTC.In the existing methods,the environmental information inputted into the neural network has many dimensions,and the correlation between the environment and the channel is unclear,resulting in a highly complex relationship construction process.To solve this issue,we propose a unified construction method of radio environment knowledge(REK)inspired by the electromagnetic wave property to quantify the propagation contribution based on easily obtainable location information.An effective scatterer determination scheme based on random geometry is proposed which reduces redundancy by 90%,87%,and 81%in scenarios with complete openness,impending blockage,and complete blockage,respectively.We also conduct a path loss prediction task based on a lightweight convolutional neural network(CNN)employing a simple two-layer convolutional structure to validate REK’s effectiveness.The results show that only 4 ms of testing time is needed with a prediction error of 0.3,effectively reducing the network complexity.
