A distributed interference control scheme in large cellular networks using mean-field game theory
Bibliographic record
Abstract
Considering a dense cellular network with a large number of base stations, this paper proposes an intercell interference control scheme using mean field game theory. Mean field game theory has been proven to be a more tractable technique than the traditional game theory. In the process of formulating the mean field game, we exploit statistical physics results to decouple the complex system of micorcells into several entities. These entities (players) are symmetric in terms of their action sets, and are interdependent by a consistent condition such that the interaction among them can be controlled. Moreover, Each entity is made capable of collecting brief and sufficient information about the system. Game theory and economic concepts are then used to decide the best transmit power. Fairness and bit error rate have been captured in the model. In different network topologies, the simulation results show that the proposed scheme achieves much better tradeoff between spectral efficiency and energy efficiency compared to different frequency reuse patterns.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".