A game theoretic approach to power trading in cognitive radio systems
Bibliographic record
Abstract
Cognitive radio (CR) has been introduced to accommodate the steady increment in the spectrum demand. In CR networks, unlicensed users, which are referred to as secondary users (SUs), are allowed to dynamically access the frequency bands when licensed users which are referred to as primary users (PUs) are inactive. One of the most important issues in CR networks is how to share the spectrum effectively among the different users which is denoted as spectrum trading. Spectrum trading aims to satisfy the objectives of both types of users (i.e. PUs and SUs) by balancing these overlapping objectives. In this paper, we propose a noncooperative game theoretic model to allow PUs to gain high profit from renting their unused frequency channels to SUs which use proper power levels over these channels for their data transmission. The proposed model will finally converge to Nash equilibrium (NE) by following the best response dynamics. Choosing the best response strategy by each game player (i.e. PU and SU) based on the perceived opponent strategies is also shown in this paper. Simulation results show that the proposed model allows PUs to make extra revenue and satisfy the quality of service (QoS) of SUs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".