Network assisted auctioning for cognitive radios
Bibliographic record
Abstract
In this paper, the use of a centralized server to assist cognitive radio users in accessing bands in licensed spectrums is proposed. Typical cognitive radios are opportunistic users of spectrum bands. Therefore, they must scan the spectrum to detect existing users in order to avoid interference. The use of a centralized server can remove the need for spectrum scanning if all users inform the server about their presence. The server will coordinate and distribute channels to cognitive radio users using auctioning mechanisms. Our approach removes the need for cognitive radio users to spectrum scan. Scanning can be costly in terms of time and power consumption. In addition, collisions between users due to hidden node problem can be removed. The use of a centralized server allows for higher layer solution that would allow users of different wireless technologies to communicate. Due to its flexibility of use across different wireless networks, SIP is adopted as the communication protocol between the central server and the primary and secondary users of the licensed spectrum. Using a SIP server to coordinate channel allocation through auctioning approach can generate revenue for the incumbent network. Results show that revenue can be generated while still meeting the goal of efficient spectrum utilization.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".