Reinforcement‐learning‐based self‐organisation for cell configuration in multimedia mobile networks
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Bibliographic record
Abstract
Abstract In future wireless code division multiple access (WCDMA) cellular networks, random user mobility and time‐varying multimedia traffic activity make the system design of coverage and capacity become a challenging issue. To utilise radio resource efficiently, it is crucial for cellular networks to have the capability of self‐organisation for cell configuration, which can configure service coverage and system capacity dynamically to balance traffic loads among cells by being aware of the system situation. This paper proposes a reinforcement‐learning‐based self‐organisation scheme for cell configuration in multimedia mobile networks, which takes into account both pilot power allocation and call admission control mechanisms. Simulation results show that the proposed scheme improves system performance significantly compared to the conventional fixed pilot power allocation scheme and the scheme in which only pilot power is adjusted dynamically but the criterion of the call admission control is not coupled to it. Copyright © 2005 AEIT.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it