Analysis of a Distance-Based Pairing Scheme for Collaborative Content Distribution via Device-to-Device Communications
Bibliographic record
Abstract
With the increasing penetration of smart devices, device-to-device (D2D) communications offer a promising paradigm to accommodate the ever-growing mobile video traffic and unremitting demands for fast content distribution. The redundant storage and communication capacities of smart devices can be exploited for collaborative content caching and distribution. Such united resources at the network edge can serve end users with low delivery cost and high performance. In this paper, we analyze a heuristic distance-based scheme, which appropriately pairs a device requesting a content item with a cache device within a collaboration distance and providing the requested content. An analytical framework is developed to derive the optimal collaboration distance so as to maximize the likelihood that the paired cache devices successfully fulfill the content requests. The simulation results validate the analytical framework and the effectiveness of the heuristic distance-based scheme for D2D-assisted content distribution. We also investigate the effects of various system parameters on the performance in terms of expected satisfaction probability.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".