Dynamic radio coordination for improved quality of experience in software-defined wireless networks
Bibliographic record
Abstract
Co-channel interference (CCI) is one of the major hindering factors of wireless system capacity and quality of experience (QoE) to users. Radio coordination (RC) techniques have been investigated to control CCI. Clustering, as a bootstrapping step of RC, defines which subset of radio nodes should be coordinated together. As a typical RC technique, cluster-based power control is not well studied in the literature. Existing solutions include all radio nodes in a single cluster, or they rely on pre-defined fixed clusters. In this tutorial we put forward dynamic clustering for coordinated power control, in the context of software-defined radio access networks, and hope to trigger more follow-up research on the topic. We propose to form clusters according to the dominant interference relation among radio nodes. A comparative simulation study indicates that the proposed approach offers similar QoE to users as the benchmark algorithms used, yet with greatly reduced RC complexity.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".