Dynamic user-AP association for QoS-aware CSMA in wireless virtualized networks
Bibliographic record
Abstract
In this paper, we investigate the dynamic user-AP association with QoS-aware CSMA in a virtualized wireless network. In particular, to efficiently support slice isolation, we consider an enhanced CSMA frame with two phases: a contention-free A-phase using CSMA with deterministic back-off, followed by a contention-based C-phase using p-persistent CSMA. The user scheduling in the contention-free A-phase and determination of p values for the contention-based C-phase along with the user-AP association are formulated as an optimization problem, aiming to maximize the overall network throughput under the slice isolation constraints and channel conditions. Subsequently, complementary geometric programming (CGP) and monomial approximations are used to develop an iterative algorithm for solution. Illustrative simulation results show the efficiency of the developed solution and its effect on network performance while maintaining the isolation among slices.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".