Ascending-Price Progressive Spectrum Auction for Cognitive Radio Networks With Power-Constrained Multiradio Secondary Users
Bibliographic record
Abstract
In this paper, we investigate spectrum sharing with power-constrained multiradio secondary users (SUs) in cognitive radio networks. The scenario under consideration consists of a primary spectrum owner who runs auctions for leasing her idle channels and multiple SU bidding for winning the usage of spectrum channels. Different from existing works in the literature with an assumption of single-minded SUs, in this paper, SUs can benefit from flexible quantity of channels. In addition, since each SU is ordinarily equipped with a fixed number of radios, she cannot utilize the amount of channels that exceed her radio capacity. Moreover, each SU has a certain power limitation so that the quality of service (QoS) of her transmission may also be constrained, even though the number of allocated channels is increased. To jointly address all these challenges, a novel ascending-price progressive auction algorithm is proposed, where the spectrum allocation decisions are made by gradually increasing the unit channel price. Theoretical analyses prove that the proposed algorithm meets the properties of QoS satisfaction, individual rationality, and incentive compatibility and achieves Pareto optimality. Simulation results further demonstrate that the proposed auction algorithm can improve both the auction revenue and the social welfare, and increase the number of winning SUs compared to the 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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".