5G wireless network: MyNET and SONAC
Bibliographic record
Abstract
Future 5G wireless networks will face new challenges, including increasing demand on network capacity to support a large number of devices running applications requiring high data rates and always-on connectivity; immensely diverse service requirements and characteristics; and supporting the emerging business models in the wireless network market requiring networks to be more open. New challenges drive new solutions and require different strategies in the network deployment, management, and operation of future 5G wireless networks compared to those of current wireless networks. One of the key objectives of future 5G wireless networks is to flexibly provide service-customized networks to a wide variety of services using the integrated cloud resource and wireless/wired network resources, which may be offered by multiple infrastructure providers and/or operators. In this article, we describe a novel wireless network architecture, MyNET, and one of the key enabling techniques called SONAC. In MyNET, basic logical functions are identified for both the control plane and the data plane. These basic functions include existing network functions, with some extensions/enhancements, as well as new network functions. SONAC selects and deploys a subset of these functions to provide customized network services.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".