Online spectrum auction in cognitive radio networks with uncertain activities of primary users
Bibliographic record
Abstract
In this paper, we investigate an online spectrum auction problem in cognitive radio networks with uncertain activities of primary users (PUs). In our framework, a primary base station (PBS), acted as the spectrum auctioneer, leases its under-utilized channels to secondary users (SUs) who request and access spectrum on the fly. Different from most of existing works in online spectrum allocation, we focus on a more practical situation that the auctioneer (or the PBS) has no prior knowledge of PUs' activities so that its channel states are not static. In order to balance the auction profits from granted SUs' spectrum requests and the potential penalties caused by incomplete services to PUs, we introduce the idea of virtual spectrum sellers and formulate the problem as an online double spectrum auction. We then propose a novel online admission and pricing mechanism which also considers the reusability of wireless spectrum. Theoretical analyses are provided to prove that our auction algorithm satisfies all desired economic properties in terms of budget-balance, individual rationality and truthfulness. Simulation results show that our proposed auction algorithm can increase the utility of the PBS, enhance spectrum utilization and achieve better satisfaction for SUs compared to counterparts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".