TPAHS: A Truthful and Profit Maximizing Double Auction for Heterogeneous Spectrums
Bibliographic record
Abstract
In recent years, the auction has been widely applied in wireless communications for spectrum allocation, so that spectrum owners can lease their unutilized spectrum to secondary users. Both primary users and secondary users can get benefit from the auction. Moreover, the spectrum utilization is improved. Existing auction mechanisms either do not consider the heterogeneity of spectrums or pay little attention to the auction's economic efficiency. In this paper, we propose a Truthful and Profit maximizing double Auction for Heterogeneous Spectrums (TPAHS), which simultaneously considers spectrum heterogeneity and economic efficiency. Moreover, different from the most existing spectrum auction mechanisms which are based on interference graph, we consider a more realistic SINR (Signal-to-Interference-plus-Noise Ratio) model. We prove that TPAHS is truthful, individual-rational, budget-balanced and the experiments show that TPAHS improves the auctioneer's profit significantly.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".