Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312121483> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W4312121483 abstract "Radio access network (RAN) slicing is a key element in enabling current 5G networks and next-generation networks to meet the requirements of different services in various verticals. However, the heterogeneous nature of these services' requirements, along with the limited RAN resources, makes the RAN slicing very complex. Indeed, the challenge that mobile virtual network operators (MVNOs) face is to rapidly adapt their RAN slicing strategies to the frequent changes of the environment constraints and service requirements. Machine learning techniques, such as deep reinforcement learning (DRL), are increasingly considered a key enabler for automating the management and orchestration of RAN slicing operations. Nerveless, the ability to generalize DRL models to multiple RAN slicing environments may be limited, due to their strong dependence on the environment data on which they are trained. Federated learning enables MVNOs to leverage more diverse training inputs for DRL without the high cost of collecting this data from different RANs. In this paper, we propose a federated deep reinforcement learning approach for RAN slicing. In this approach, MVNOs collaborate to improve the performance of their DRL-based RAN slicing models. Each MVNO trains a DRL model and sends it for aggregation. The aggregated model is then sent back to each MVNO for immediate use and further training. The simulation results show the effectiveness of the proposed DRL approach." @default.
- W4312121483 created "2023-01-04" @default.
- W4312121483 creator A5011549008 @default.
- W4312121483 creator A5060400736 @default.
- W4312121483 creator A5085082908 @default.
- W4312121483 creator A5091388380 @default.
- W4312121483 date "2022-06-22" @default.
- W4312121483 modified "2023-09-26" @default.
- W4312121483 title "Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks" @default.
- W4312121483 doi "https://doi.org/10.48550/arxiv.2206.11328" @default.
- W4312121483 hasPublicationYear "2022" @default.
- W4312121483 type Work @default.
- W4312121483 citedByCount "0" @default.
- W4312121483 crossrefType "posted-content" @default.
- W4312121483 hasAuthorship W4312121483A5011549008 @default.
- W4312121483 hasAuthorship W4312121483A5060400736 @default.
- W4312121483 hasAuthorship W4312121483A5085082908 @default.
- W4312121483 hasAuthorship W4312121483A5091388380 @default.
- W4312121483 hasBestOaLocation W43121214831 @default.
- W4312121483 hasConcept C106365562 @default.
- W4312121483 hasConcept C108583219 @default.
- W4312121483 hasConcept C111919701 @default.
- W4312121483 hasConcept C120314980 @default.
- W4312121483 hasConcept C136764020 @default.
- W4312121483 hasConcept C142362112 @default.
- W4312121483 hasConcept C153083717 @default.
- W4312121483 hasConcept C153349607 @default.
- W4312121483 hasConcept C154945302 @default.
- W4312121483 hasConcept C160704184 @default.
- W4312121483 hasConcept C199168358 @default.
- W4312121483 hasConcept C207029474 @default.
- W4312121483 hasConcept C26517878 @default.
- W4312121483 hasConcept C2776190703 @default.
- W4312121483 hasConcept C2779765720 @default.
- W4312121483 hasConcept C31258907 @default.
- W4312121483 hasConcept C41008148 @default.
- W4312121483 hasConcept C558565934 @default.
- W4312121483 hasConcept C68649174 @default.
- W4312121483 hasConcept C97541855 @default.
- W4312121483 hasConceptScore W4312121483C106365562 @default.
- W4312121483 hasConceptScore W4312121483C108583219 @default.
- W4312121483 hasConceptScore W4312121483C111919701 @default.
- W4312121483 hasConceptScore W4312121483C120314980 @default.
- W4312121483 hasConceptScore W4312121483C136764020 @default.
- W4312121483 hasConceptScore W4312121483C142362112 @default.
- W4312121483 hasConceptScore W4312121483C153083717 @default.
- W4312121483 hasConceptScore W4312121483C153349607 @default.
- W4312121483 hasConceptScore W4312121483C154945302 @default.
- W4312121483 hasConceptScore W4312121483C160704184 @default.
- W4312121483 hasConceptScore W4312121483C199168358 @default.
- W4312121483 hasConceptScore W4312121483C207029474 @default.
- W4312121483 hasConceptScore W4312121483C26517878 @default.
- W4312121483 hasConceptScore W4312121483C2776190703 @default.
- W4312121483 hasConceptScore W4312121483C2779765720 @default.
- W4312121483 hasConceptScore W4312121483C31258907 @default.
- W4312121483 hasConceptScore W4312121483C41008148 @default.
- W4312121483 hasConceptScore W4312121483C558565934 @default.
- W4312121483 hasConceptScore W4312121483C68649174 @default.
- W4312121483 hasConceptScore W4312121483C97541855 @default.
- W4312121483 hasLocation W43121214831 @default.
- W4312121483 hasOpenAccess W4312121483 @default.
- W4312121483 hasPrimaryLocation W43121214831 @default.
- W4312121483 hasRelatedWork W2037995385 @default.
- W4312121483 hasRelatedWork W2625909145 @default.
- W4312121483 hasRelatedWork W2943323395 @default.
- W4312121483 hasRelatedWork W3132331930 @default.
- W4312121483 hasRelatedWork W3135104520 @default.
- W4312121483 hasRelatedWork W3214651724 @default.
- W4312121483 hasRelatedWork W4285794073 @default.
- W4312121483 hasRelatedWork W4285816540 @default.
- W4312121483 hasRelatedWork W4286858889 @default.
- W4312121483 hasRelatedWork W4287180973 @default.
- W4312121483 isParatext "false" @default.
- W4312121483 isRetracted "false" @default.
- W4312121483 workType "article" @default.