A Survey on Intent-Driven End-to-End 6G Mobile Communication System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it