Robust Multiuser Sequential Channel Sensing and Access in Dynamic Cognitive Radio Networks: Potential Games and Stochastic Learning
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Bibliographic record
Abstract
This paper studies the problem of multiuser sequential channel sensing and access in dynamic cognitive radio networks, in which the active-user set is randomly changing from slot to slot. Furthermore, each user only has its individual information with no information exchange among users. The goal of the users is to determine their channel sensing order. We first define a generalized interference metric to address the overlapping of channel sensing order and establish two optimization objectives: minimizing the aggregate interference for each active-user set and minimizing the expected aggregate interference for all potential users. It is challenging to solve the two optimization problems, even in a centralized manner, because the active-user set is randomly changing, and the probability distributions of the active-user sets are unknown to the users. We then propose two noncooperative game models to solve the optimization problems: a state-based one-shot game and a robust game. We prove that they are potential games and that the best Nash equilibrium of the two games corresponds to the optimal solutions of the two optimization problems, respectively. To cope with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uncertain</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incomplete </i> information constraints in the dynamic networks, we propose a stochastic learning algorithm, which is analytically proven to converge to Nash equilibria of the two formulated games in the presence of a changing active-player set. Finally, simulation results are presented to validate the convergence and superior performance of the proposed learning algorithm.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it