Downlink cell association optimization for heterogeneous networks via dual coordinate descent
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers the optimal association of remote user terminals to different cells in a heterogeneous network for load balancing. Assuming fixed transmit powers at the base-stations, we adopt a network utility maximization formulation with a proportional fairness objective and show that the downlink user association problem can be solved efficiently using a pricing approach where the prices are updated in the dual domain via coordinate descent. As compared to the previously proposed subgradient method, the proposed coordinate descent algorithm does not require the base-stations to synchronize in their price updates, while still guaranteeing convergence, which makes it particularly suitable for distributed implementation. Simulations show that the proposed method has fast convergence while achieving near-optimal solution.
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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 | 0.000 | 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 | 0.000 | 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 it