AppRAN: Application-oriented radio access network sharing in mobile networks
Bibliographic record
Abstract
As a promising way to increase network capacity and reduce expenses, radio access network (RAN) sharing among mobile (virtual) network operators, has attracted extensive recent attention from both industry and academia. Meanwhile, mobile systems are undergoing fast evolution to virtualized infrastructure so as to tackle the ever-growing mobile traffic and the unremitting demand for high data rates. However, existing RAN sharing models intend to expose resource details, e.g., infrastructure and spectrum, to participating network operators of the RAN for resource-sharing purposes, which violates the principles of network abstraction and makes network management even more complicated. This paper presents AppRAN, an application-oriented framework for RAN sharing in mobile networks, which decouples network operators from radio resource by providing application-level services with Quality of Service (QoS) guarantee. AppRAN defines a serial of abstract applications with distinct QoS requirements and periodically computes application-level resource allocation for each radio element at a central controller w.r.t. traffic demands and average channel condition. The radio elements are allowed to independently determine flow-level resource allocation within each application afterwards. We formulate the application-level resource allocation as an optimization problem and develop a fast algorithm to solve it with a provably approximate guarantee. The efficacy of AppRAN is validated through theoretical analysis and computer simulations. We show that AppRAN is in line with the design of software-defined RAN.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".