A price setting approach to power trading in cognitive radio networks
Bibliographic record
Abstract
Cognitive radio technology has been proposed to improve the spectrum utilization by sharing the frequency spectrum bands between the licensed and unlicensed users which are called primary users (PUs) and secondary users (SUs) respectively. The main objective of the SUs is to achieve their QoS by exploiting the unused spectrum while the PUs aim to get high profit by leasing their unused spectrum. Pricing and transmission power are two key issues of interest to PUs and SUs as well. In this paper, we propose a power pricing model wherein the PUs attain some revenue by renting their unused frequency to SUs that use suitable power levels to transmit which do not interfere with other users in the network. In our proposed model the SUs coexist with PUs in the same network where they can transmit over the same channel simultaneously. Performance evaluation of the proposed model demonstrates that the scheme helps in using the frequency spectrum more efficiently and increasing the gained profit of the PUs in comparison with the other existing models.
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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 | 0.003 | 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 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".