A Game-Theoretic Approach for Optimal Distributed Cooperative Hybrid Caching in D2D Networks
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Bibliographic record
Abstract
The distributed cooperative hybrid caching problem based on content-awareness in device-to-device networks is studied in this letter. Besides caching from the base station, nodes also can cache files from nearby nodes. We also consider the content similarity between caching nodes, which would reduce the cost further through caching similar traffic from one source cooperatively. We model the cost reducing problem as a local cooperative game, and prove it to be an exact potential game, which has at least one pure Nash equilibrium (NE). Fortunately, the potential function is just the aggregate cost of the network, which means the NE point minimizes the total cost. We modified the log-linear learning algorithm and designed a half-fixed action to reduce the strategy space, and with random action to pursue better performance. The simulation results show that the modified log-linear learning algorithm achieves better performance compared with other algorithms, and the content-aware hybrid caching reduces the cost.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.001 |
| 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 it