Revenue-maximizing and truthful online auctions for dynamic spectrum access
Bibliographic record
Abstract
Secondary spectrum auctions have been suggested as a strategically robust mechanism for distributing idle spectrum to competing secondary users. However, previous work on such auction design have assumed a static auction setting, thus failing to fully exploit the inherently time-varying nature of spectrum demand and utilization. In this paper, we address this issue from the perspective of the primary user who wishes to maximize the auction revenue. We present an online auction framework that dynamically accepts bids and allocates spectrum. We prove rigorously that our online auction framework is truthful in the multiple dimensions of bid values, as well as bid timing parameters. To protect against unbounded loss of revenue due to latter bids, we introduce controlled preemption into our mechanism. We prove that preemption, coupled with the technique of inflating bids artificially, leads to an online auction that guarantees a 1/5-fraction of the optimal revenue as obtained by an offline adversary. Since the previous guarantee holds only for the optimal channel allocation, we further provide a greedy channel allocation scheme which provides scalability. We prove that the greedy scheme also obtains a constant competitive revenue guarantee, where the constant depends on the parameter of the conflict graph.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".