FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4210368370> ?p ?o ?g. }

• W4210368370 endingPage "175" @default.
• W4210368370 startingPage "159" @default.
• W4210368370 abstract "Due to the greatly increased bandwidth of 5G networks compared with that of 4G networks, the power consumption brought by baseband signal processing of 5G networks is much higher, which inevitably raises the operation expenditures. Cloud Radio Access Network (CRAN) is widely adopted in 5G networks, which splits the traditional base stations into Remote Radio Heads (RRHs) and Baseband Units (BBUs), which are equipped with computing resource for baseband signal processing. The number of required BBUs varies due to the fluctuation of wireless traffic of RRHs. Hence, fixed computing resource allocation might waste power. This paper investigates energy-efficient dynamic computing resource allocation in CRAN by predicting the wireless traffic of RRHs and allocating computing resource based on the prediction results aiming at using fewest BBUs to minimize power consumption. For wireless traffic prediction, a novel method based on two-dimensional CNN LSTM model with temporal aggregation is proposed. By treating the wireless traffic data as images, this model could extract spatial correlation from these data to improve accuracy. Moreover, the problem of dynamic computing resource allocation in CRAN is formulated as an offline four-constraint bin packing problem, considering both uplink and downlink baseband signal processing capacities of BBUs and Common Public Radio Interface (CPRI) bandwidths. For solving this problem, a Multi-start Simulated Annealing (MSA) algorithm is proposed. Simulation results demonstrate that the proposed method for wireless traffic prediction could outperform the state-of-the-art deep learning models. In addition, the proposed MSA algorithm could achieve lower power consumption than the state-of-the-art heuristic algorithms." @default.
• W4210368370 created "2022-02-08" @default.
• W4210368370 creator A5034619681 @default.
• W4210368370 creator A5065443071 @default.
• W4210368370 date "2022-01-01" @default.
• W4210368370 modified "2023-10-05" @default.
• W4210368370 title "Traffic Prediction-Enabled Energy-Efficient Dynamic Computing Resource Allocation in CRAN Based on Deep Learning" @default.
• W4210368370 cites W1519941054 @default.
• W4210368370 cites W1968112823 @default.
• W4210368370 cites W1980373651 @default.
• W4210368370 cites W1994874668 @default.
• W4210368370 cites W2006091920 @default.
• W4210368370 cites W2056950965 @default.
• W4210368370 cites W2111440106 @default.
• W4210368370 cites W2118943694 @default.
• W4210368370 cites W2126831543 @default.
• W4210368370 cites W2140037265 @default.
• W4210368370 cites W2242927635 @default.
• W4210368370 cites W2283113811 @default.
• W4210368370 cites W2493206205 @default.
• W4210368370 cites W2495887696 @default.
• W4210368370 cites W2607038276 @default.
• W4210368370 cites W2613328025 @default.
• W4210368370 cites W2744723246 @default.
• W4210368370 cites W2768775141 @default.
• W4210368370 cites W2768949383 @default.
• W4210368370 cites W2771499184 @default.
• W4210368370 cites W2807536558 @default.
• W4210368370 cites W2888165363 @default.
• W4210368370 cites W2895854315 @default.
• W4210368370 cites W2898951443 @default.
• W4210368370 cites W2921319277 @default.
• W4210368370 cites W2941572705 @default.
• W4210368370 cites W2948061880 @default.
• W4210368370 cites W2963035276 @default.
• W4210368370 cites W2971262151 @default.
• W4210368370 cites W3013008552 @default.
• W4210368370 cites W3033902456 @default.
• W4210368370 cites W3033989372 @default.
• W4210368370 cites W3045867723 @default.
• W4210368370 cites W3046072278 @default.
• W4210368370 cites W3047104333 @default.
• W4210368370 cites W3089209260 @default.
• W4210368370 cites W3096577108 @default.
• W4210368370 cites W3103833233 @default.
• W4210368370 cites W3117922432 @default.
• W4210368370 cites W3121151732 @default.
• W4210368370 cites W3156128181 @default.
• W4210368370 cites W3180961361 @default.
• W4210368370 cites W90213227 @default.
• W4210368370 doi "https://doi.org/10.1109/ojcoms.2022.3146886" @default.
• W4210368370 hasPublicationYear "2022" @default.
• W4210368370 type Work @default.
• W4210368370 citedByCount "5" @default.
• W4210368370 countsByYear W42103683702022 @default.
• W4210368370 countsByYear W42103683702023 @default.
• W4210368370 crossrefType "journal-article" @default.
• W4210368370 hasAuthorship W4210368370A5034619681 @default.
• W4210368370 hasAuthorship W4210368370A5065443071 @default.
• W4210368370 hasBestOaLocation W42103683701 @default.
• W4210368370 hasConcept C106365562 @default.
• W4210368370 hasConcept C108037233 @default.
• W4210368370 hasConcept C111919701 @default.
• W4210368370 hasConcept C119599485 @default.
• W4210368370 hasConcept C120314980 @default.
• W4210368370 hasConcept C127413603 @default.
• W4210368370 hasConcept C138660444 @default.
• W4210368370 hasConcept C153646914 @default.
• W4210368370 hasConcept C207029474 @default.
• W4210368370 hasConcept C2776257435 @default.
• W4210368370 hasConcept C2780165032 @default.
• W4210368370 hasConcept C29202148 @default.
• W4210368370 hasConcept C31258907 @default.
• W4210368370 hasConcept C41008148 @default.
• W4210368370 hasConcept C555944384 @default.
• W4210368370 hasConcept C65165936 @default.
• W4210368370 hasConcept C68649174 @default.
• W4210368370 hasConcept C76155785 @default.
• W4210368370 hasConcept C79403827 @default.
• W4210368370 hasConcept C79974875 @default.
• W4210368370 hasConceptScore W4210368370C106365562 @default.
• W4210368370 hasConceptScore W4210368370C108037233 @default.
• W4210368370 hasConceptScore W4210368370C111919701 @default.
• W4210368370 hasConceptScore W4210368370C119599485 @default.
• W4210368370 hasConceptScore W4210368370C120314980 @default.
• W4210368370 hasConceptScore W4210368370C127413603 @default.
• W4210368370 hasConceptScore W4210368370C138660444 @default.
• W4210368370 hasConceptScore W4210368370C153646914 @default.
• W4210368370 hasConceptScore W4210368370C207029474 @default.
• W4210368370 hasConceptScore W4210368370C2776257435 @default.
• W4210368370 hasConceptScore W4210368370C2780165032 @default.
• W4210368370 hasConceptScore W4210368370C29202148 @default.
• W4210368370 hasConceptScore W4210368370C31258907 @default.
• W4210368370 hasConceptScore W4210368370C41008148 @default.
• W4210368370 hasConceptScore W4210368370C555944384 @default.
• W4210368370 hasConceptScore W4210368370C65165936 @default.
• W4210368370 hasConceptScore W4210368370C68649174 @default.
• W4210368370 hasConceptScore W4210368370C76155785 @default.

• next