GEN02-1: Hierarchical Iterative Algorithm for a Coupled Constrained OSNR Nash Game
Bibliographic record
Abstract
This paper develops a hierarchical iterative OSNR algorithm based on a game theory framework. A Nash game is formulated between channels with channel utility related to maximizing channel optical signal-to-noise ratio (OSNR). The OSNR game has coupled utilities and coupled constraints, such that total power is kept below the nonlinearity threshold. Solving directly this game requires coordination among all channels and is impractical in networks. A duality approach is used instead, based on the recent theoretical results in [16]. This method offers a natural way to hierarchically decompose the coupled Nash game into a lower-level Nash game with no coupled constraints, and a higher-level link optimization problem for pricing parameters. The lower-level Nash game is analytically tractable, and its solution can be iteratively found via an algorithm decentralized with respect to channels. The price is adjusted at the network higher-level so that channels are induced to cooperate towards satisfying the coupled total power constraint.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".