Distributed Channel Selection in Time-Varying Radio Environment: Interference Mitigation Game With Uncoupled Stochastic Learning
Bibliographic record
Abstract
This paper investigates the problem of distributed channel selection for interference mitigation in a time-varying radio environment without information exchange. Most existing algorithms, which were originally designed for static channels, are costly and inefficient in the presence of time-varying channels. First, we formulate this problem as a noncooperative game, in which the utility of each player is defined as a function of its experienced expected weighted interference. This game is proven to be an exact potential game with the considered network utility (the expected weighted aggregate interference) serving as the potential function. However, most game-theoretic algorithms are not suitable for the considered network, since they are coupled, i.e., the updating procedure is relying on the actions or payoffs of other players. Then, we propose a simple, completely distributed, and uncoupled stochastic learning algorithm, with which the users learn the desirable channel selections from their individual trial-payoff history. It is analytically shown that the proposed algorithm converges to pure strategy Nash equilibrium in time-varying radio environment; moreover, it achieves optimal channel selection profiles and makes the network interference-free for underloaded or equally loaded scenarios, while achieving, on average, near-optimal performance for overloaded scenarios.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".