A Survey on Intent-Driven End-to-End 6G Mobile Communication System
Bibliographic record
Abstract
With the virtualization, intelligence, and autonomous features driving the development of end-to-end (E2E) 6th-generation (6G) mobile communication systems, on-demand resilient network orchestration and configuration are becoming increasingly complicated. Meanwhile, due to human involvement, network complexity grows exponentially while its scalability remains constrained. There is an urgent need for novel networking paradigms to facilitate network management and control, particularly with service multiplicity and network dynamics. Intent-Driven Network (IDN) is essential for addressing these challenges and enhancing network scalability. Although the IDN has attracted wider research attention, there is a lack of a systematic review and comprehensive survey to clarify the basics and summarize the state of the art of IDN research status. In this survey, we investigate and provide an overview of the applications of the IDN. First, we discuss the intent-driven E2E 6G mobile communication system framework, and then introduce the key components of this framework. Second, IDN design and applications from an E2E 6G mobile communication system perspective are investigated and surveyed. Moreover, we discuss the full-life cycle management of generic IDN techniques, contributing to reducing network complexity. This survey will continue to review the research advances in other network paradigms related to IDN design and applications. Finally, we conclude this survey with open issues, challenges, and future research directions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".