A prior-free revenue maximizing auction for secondary spectrum access
Bibliographic record
Abstract
Dynamic spectrum allocation has proven promising for mitigating the spectrum scarcity problem. In this model, primary users lease chunks of under-utilized spectrum to secondary users, on a short-term basis. Primary users may need financial motivations to share spectrum, since they assume costs in obtaining spectrum licenses. Auctions are a natural revenue generating mechanism to apply. Recent design on spectrum auctions make the strong assumption that the primary user knows the probability distribution of user valuations. We study revenue-maximizing spectrum auctions in the more realistic prior-free setting, when information on user valuations is unavailable. A two-phase auction framework is constructed. In phase one, we design a strategyproof mechanism that computes a subset of users with an interference-free spectrum allocation, such that the potential revenue in the second phase is maximized. A tailored payment scheme ensures truthful bidding at this stage. The selected users then participate in phase two, where we design a randomized competitive auction and prove its strategyproofness through the argument of bid independence. Employing probabilistic techniques, we prove that our auction generates a revenue that is at least 1/3 of the optimal revenue, improving the best known ratio of 1/4 proven for similar settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".