IDSM: Intent-Driven Slice Management and Maintenance for 6G RANs
Bibliographic record
Abstract
The next-generation wireless communication networks need to support various vertical industries and emerging use cases, with diverse services making the network architecture increasingly complex. Network slicing enables service-oriented management and maintenance by dividing the infrastructure into multiple logical networks to meet diverse and stringent requirements. However, faced with a large tenant base and personalized slicing requirements, network slice providers should put significant effort to complete the lifecycle management of the slices, which severely hinders the implementation of the large-scale end-to-end sliced network. Intent-driven network (IDN) can be used to reduce the complexity of management and maintenance for 6G Radio Access Networks (RANs) by automating the configuration and operation of managed networks with a high-level abstraction of intent. Therefore, we present the framework of Intent-driven Slice Management and Maintenance for 6G RANs, which is termed as IDSM. Firstly, we describe its architecture, followed by the three key technologies of intent translation, slice generation, and Deep Q Network (DQN) intent maintenance. Finally, OpenAirInterface (OAI) platform and FlexRAN Software-Defined-Network (SDN) controller with DQN are used to demonstrate the presented IDSM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".