An Auction-Based Time Scheduling Mechanism for Backscatter-Aided RF-Powered Cognitive Radio Networks
Bibliographic record
Abstract
In this paper, we study the time scheduling strategy for backscatter-aided radio-frequency-powered cognitive radio networks, where multiple secondary transmitters transmit data to the same secondary gateway. With more secondary transmitters connected to the networks, the time resource is becoming more congested, and the case that the total transmission demand of secondary transmitters exceeds the transmission capacity of the secondary networks may frequently occur. In this case, the secondary transmitters which can bring more social welfare to the networks should be permitted to transmit data with a high priority. Therefore, we propose an auction-based time scheduling mechanism in this paper. Specifically, we first present an auction model, where the secondary gateway acts as the seller as well as the auctioneer, and the secondary transmitters act as the buyers to bid for the time resource. We next design the strategies of the bid submission, the winner determination and time scheduling, and the price calculation for the time resource auction. Furthermore, the economic properties and the computational efficiency of the proposed mechanism are analysed. Numerical simulations are conducted to examine the performance of our proposed mechanism as well as to evaluate the impact of the valuation on the probability of winning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 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".