Introducing wireless access programmability using software-defined infrastructure
Bibliographic record
Abstract
Programmability in wireless access networks can provide unprecedented flexibility in meeting the communications needs of a diverse set of wireless devices under changing demand and network conditions. Programmability also holds the promise of enabling multiple simultaneous virtual operators providing a variety of access networks that offer different services using different business models. In this paper we present our work on enabling wireless access network programmability in the SAVI testbed. We introduce an architecture for Software-Defined Infrastructure that offers virtualized heterogeneous resources in support of services and applications. Central to this architecture is the Janus SDI resource manager that can coordinate the actions of a set of controllers, e.g. OpenStack, OpenFlow, and other controllers such as a wireless access controller. We describe a design where Janus is used to integrate the wireless access network into a Smart Edge node. We introduce use cases that exploit the flexibility offered by this design, and we present experimental results from our implementation.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".