Downlink resource allocation for data traffic in heterogenous cellular CDMA networks
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Bibliographic record
Abstract
In this paper, using the dynamic pricing platform, a novel framework for downlink resource allocation in heterogeneous cellular CDMA networks is proposed. For each user, we define a utility that is a function of channel status and delay condition of that individual user as well as network load status. The network utility is then defined as the summation of the users' achieved utilities. We solve downlink resource allocation problem through maximization of total network utility. This approach results in a suboptimal base-station assignment scheme which-unlike previous work-is network optimal instead of cell optimal. We then show that optimal base-station assignment is a multidimensional multiple-choice Knapsack problem (MMKP). Since MMKP is NP-Hard a polynomial-time suboptimal heuristic algorithm is then employed to develop an efficient base-station assignment.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| 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