A Game-Theoretic Approach for Optimal Distributed Cooperative Hybrid Caching in D2D Networks
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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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 | 0.001 | 0.003 |
| 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".