Designing Two-Dimensional Spectrum Auctions for Mobile Secondary Users
Bibliographic record
Abstract
Dynamic spectrum access by non-licensed users has emerged as a promising solution to address the bandwidth scarcity challenge. In a secondary spectrum market, primary users lease chunks of unused spectrum to secondary users. Auctions perform as one of the natural mechanisms for allocating the spectrum, generating an economic incentive for the licensed user to relinquish channels. Existing spectrum auction designs, while taking externality introduced by interference into account, fail to consider the potential mobility of secondary users, which leads to another dimension of externality: mobile communication motivates a secondary user to exclusively occupy a channel, i.e., forbidding channel reuse in its mobility region. In this work, we design two expressive auctions for mobility support, by introducing two-dimensional bids that reject a secondary user's willingness to pay for exclusive and non-exclusive channel usage, for the single-channel and multiple-channel scenarios, respectively. In the outcome of our 2D auctions, a channel is either monopolized or simultaneously reused without interference, whereas a secondary user can be mobile or is regulated to be static. We prove the existence of desirable equilibria in both auctions, where 1/10 and c/7(1+c) of optimal social welfare are guaranteed to be recoverable, respectively (c is the number of channels).
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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 | 0.004 | 0.008 |
| 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 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".