A college admissions game for uplink user association in wireless small cell networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, the problem of uplink user association in small cell networks, which involves interactions between users, small cell base stations, and macro-cell stations, having often conflicting objectives, is considered. The problem is formulated as a college admissions game with transfers in which a number of colleges, i.e., small cell and macro-cell stations seek to recruit a number of students, i.e., users. In this game, the users and access points (small cells and macro-cells) rank one another based on preference functions that capture the users' need to optimize their utilities which are functions of packet success rate (PSR) and delay as well as the small cells' incentive to extend the macro-cell coverage (e.g., via cell biasing/range expansion) while maintaining the users' quality-of-service. A distributed algorithm that combines notions from matching theory and coalitional games is proposed to solve the game. The convergence of the algorithm is shown and the properties of the resulting assignments are discussed. Simulation results show that the proposed approach yields a performance improvement, in terms of the average utility per user, reaching up to 23% relative to a conventional, best-PSR algorithm.
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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