Distributed Learning of Unknown Games for HetNet Selection
Bibliographic record
Abstract
Heterogeneous network (HetNet)selection is a challenging problem for wireless devices equipped with different radio access technologies (e.g., LTE, 5 G, and WiFi), as clients in the same network often behave selfishly to compete for the common resources to maximize their own rewards (e.g., throughput). Most existing works often model the HetNet selection problem as a non-cooperative game among clients and find strategies to achieve equilibria. However, those works often assume afull-information setting where the number, actions, or rewards of other clients are known a priori. In practice, clients may havelimitedknowledge, i.e., they only observe their own rewards in the networks they currently attach to. To address the HetNet selection problem in the limited information setting, we model the problem as anunknowngame repeated for anunknownnumber of rounds, and propose a distributed learning algorithm calledLightsto achieve correlated equilibria in a polynomial number of rounds with provable bounds. Theoretically, we present a novel concentration bound for the reward estimator used in Lights based on a martingale analysis, which is the key to proving the convergence property of Lights. Furthermore, extensive experiments are conducted to verify the performance of the proposed Lights algorithm.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".