Strategyproof Wireless Spectrum Auctions with Interference
Bibliographic record
Abstract
Wireless spectrum is a regulated resource, whose control and usage is regulated by government agencies. The allocation of spectrum to interested parties is usually conducted through auctions, and are an important source of income for these regulatory agencies. However, previous spectrum auction design fail to take into consideration the effect of interference, which can adversely affect the truthfulness of an auction. In this paper, we explicitly consider interference effects, and design truthful auctions for maximizing social welfare. Since the spectrum allocation problem is NP-Hard, we first show how to compute an approximate spectrum allocation scheme that is within a constant factor of the optimal solution, under certain simplifying assumptions on the interference graph. We then proceed to make this scheme strategyproof by tailoring a payment scheme based on the idea of minimum bids. A naive method to compute such a payment scheme requires O(n) iterations of the spectrum allocation algorithm, where n is the number of bidders. We show how to reduce the complexity to O(1) iterations instead. We conclude by discussing possible directions for future research in this area.
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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 | 0.007 | 0.028 |
| 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 | 0.004 | 0.006 |
| Open science | 0.003 | 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".