Toward an AI-Enabled SDN-based 5G & IoT Network
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
Artificial Intelligent technology has been applied to a wide variety of applications including products, systems, and services of the Information & Communications Technology (ICT) and non-ICT or traditional industries. The benefits of these applications includes performance improvement, optimization, intelligent.The 5G mobile/wireless networks have intelligent features of network slicing and edge computing because of network equipment system vendors apply AI technology to the mobile systems. On the other hand, many traditional industries have benefits from AI technology in particular Machine Learning (ML) and Deep Learning (DL). Recently in the agriculture, healthcare, finance, and many other applications and services have adopted AI/ML/DL technology even with the integration of 5G and Internet of Things (IoT). This article focuses on the system architecture and design of open networking (ON) solution of 5G, the approach of SDN/NFV-based 5G and IoT and how AI/ML interact with 5G/IoT and learns from these. We call this interaction as SDN-based 5G/IoT Network AI or AI-enabled SDN-based 5G/IoT Network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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