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5G network slicing algorithm based on prediction
Published 2021-09-01“…5G on line scenario needs short build time of network slice, it can use prediction algorithm to isolate network resource in advance to reduce the construction time of network slice.A 5G network slicing algorithm based on prediction was proposed, which used fourth square as cost function to predict the network resource requests with accepted accurate and lower complexity of algorithm.And then based on the prediction result, virtual nodes and links resource were allocated, and container was pulled up when network slice request arrives to network, the dynamic creation of network slices was completed.The simulation results show that the proposed algorithm can get 90% accurate of prediction, and reduce 50% construction time of network slice, and improve the network utilization ration，when new network slice reusing the original network slice.…”
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Towards 5G：heterogeneous cellular network architecture design based on intelligent SDN paradigm
Published 2016-06-01“…To enrich the SDN（software defined networking）with intelligence and learning capability，an intelligent SDN paradigm was proposed for the first time.Combining it with the traditional cellular networks，5G-oriented heterogeneous cellular network architecture based on the intelligent SDN was set up，which made the integrated management of radio access networks and core networks possible.Based on the management and coordination of intelligent SDN，the decoupling of control and data，decoupling of uplink and downlink，and dynamic resource utilization in radio access networks to improve the spectrum efficiency and energy efficiency were put forward；content caching and network security strategies were deployed in the core networks to enhance the network scalability and data throughput.Globally，the resource utilization in radio access networks and core networks was optimized to achieve the network function virtualization and customized service provision，which contributed to the in-depth combination between the heterogeneous cellular networks and intelligent SDN.…”
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1188
Towards 5G：heterogeneous cellular network architecture design based on intelligent SDN paradigm
Published 2016-06-01“…To enrich the SDN（software defined networking）with intelligence and learning capability，an intelligent SDN paradigm was proposed for the first time.Combining it with the traditional cellular networks，5G-oriented heterogeneous cellular network architecture based on the intelligent SDN was set up，which made the integrated management of radio access networks and core networks possible.Based on the management and coordination of intelligent SDN，the decoupling of control and data，decoupling of uplink and downlink，and dynamic resource utilization in radio access networks to improve the spectrum efficiency and energy efficiency were put forward；content caching and network security strategies were deployed in the core networks to enhance the network scalability and data throughput.Globally，the resource utilization in radio access networks and core networks was optimized to achieve the network function virtualization and customized service provision，which contributed to the in-depth combination between the heterogeneous cellular networks and intelligent SDN.…”
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Lanthanide single-atom catalysts for efficient CO2-to-CO electroreduction
Published 2025-03-01Get full text
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1191
Application of marine bacterial alginate lyases in wastewater treatment, biofilm removal and green technology
Published 2025-07-01“…These applications are further enhanced by the enzymes’ compatibility with green processing technologies and sustainable resource utilization. This study provides a comprehensive overview of marine alginate lyases, focusing on their enzymatic properties, catalytic mechanisms, and industrial relevance. …”
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Power Allocation Technology of Long Time Multi-Star Hopping Beam for LEO Satellite
Published 2023-12-01“…LEO satellites have superior development prospects due to their low cost, low latency and small path loss, and are widely used in IoT, B5G and other fields.For the LEO satellite and its coverage area will be in a moving state, a convex optimization-based long-time multi-star beam hopping power allocation algorithm was proposed to maximize the system capacity.Focused on the multi-star hopping beam scenario over a period of time, a system model was developed based on the long-time co-orbital multi-star hopping beam scenario and the long-time heterodyne multi-star hopping beam scenario respectively.The resource allocation algorithm was designed for the two long-time multi-star hopping beams with the weighted objective function as the optimization objective, considered the influence factors of inter-star interference, load balancing and inter-star resource allocation priority, a long-time skipping beam resource allocation algorithm based on convex optimization was proposed.The simulation results showed that the proposed scheme could improve the resource utilization of the system compared with the conventional schemes.…”
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Load balancing method of service cluster based on mean-variance
Published 2017-01-01“…When a large number of concurrent requests are allocated,the load scheduling mechanism is to achieve the load balancing of nodes in the network by minimizing the response time and maximizing the utilization ratio of nodes.In the load balancing algorithm based on genetic algorithm,the fitness function is designed to have an important influence on the load balancing efficiency.A service cluster load balancing method based on mean-variance was proposed to optimize the fitness function.The investment portfolio selection model mean-variance was used to minimize the response time,which was used to get the weight of each server's resource utilization,so as to obtain the optimal allocation combination.This method improves the accuracy and efficiency of the fitness function.Compared with other models in different service environment,the simulation results show that the load balancing algorithm makes the service cluster get a better balance performance in terms of node utilization and response time.…”
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