Combinatorial spectrum auction with multiple heterogeneous sellers in cognitive radio networks
Why this work is in the frame
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Bibliographic record
Abstract
Spectrum auction has been considered as an economically incentive way to motivate both primary spectrum owners (POs) and secondary users (SUs) to participate in dynamic spectrum access (DSA). In this paper, we propose a new combinatorial spectrum auction framework for the scenarios that each PO has multiple channels to sell and each SU demands multiple channels. Moreover, the heterogeneity in terms of POs' channel bandwidths and SUs' demands is also considered. The winner determination problem (WDP) in the proposed auction framework can be formulated as a multiple multidimensional knapsack problem (MMKP) and both upper bound and an approximation algorithm with polynomial time are developed. A tailored pricing mechanism is adopted in the payment design to ensure truthfulness and individual rationality. Numerical results show that our proposed auction algorithm can improve the spectrum allocation efficiency compared to counterparts.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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