Distributed resource allocation for self-organizing small cell networks: An evolutionary game approach
Bibliographic record
Abstract
Future wireless networks are expected to be highly heterogeneous with the co-existence of macrocells and a large number of small cells. In this case, centralized control and manual intervention will be highly inefficient. Therefore, self organization and distributed resource allocation are of paramount importance for the successful deployment of small cell networks. In this work, we propose an evolutionary game theory (EGT)-based distributed resource allocation scheme for small cell networks. EGT is a suitable tool to address the self organized small cell resource allocation problem since it allows the players with bounded rationality to make individual decisions and learn from the environment for attaining the equilibrium with the minimum information exchange. Also, fairness can be provided. Specifically, we show how EGT can be used for subcarrier and power allocation of small cell networks. Replicator dynamics is used to model the strategy adaptation process of the small cell base stations and the evolutionary equilibrium is obtained as the solution. Numerical results show the effectiveness of the proposed scheme.
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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".