Cross-Layer Network Codes for Content Delivery in Cache-Enabled D2D Networks
Bibliographic record
Abstract
In this paper, we consider the use of cross-layer network coding (CLNC), caching, and device-to-device (D2D) communications to jointly optimize the delivery of a set of popular contents to a set of user devices (UDs). In the considered D2D network, a group of near-by UDs cooperate with each other and use NC to combine their cached files, so as the completion time required for delivering all requested contents to all UDs is minimized. Unlike the previous work that considers only one transmitting UD at a time, our work allows multiple UDs to transmit simultaneously given the interference among the active links is small. Such configuration brings a new trade-off among scheduling UDs to transmitting UDs, selecting the coding decisions and the transmission rate/power. Therefore, we consider the completion time minimization problem that involves scheduling multiple transmitting UDs, determining their transmission rates/powers and file combinations. The problem is shown to be intractable because it involves all future coding decisions. To tackle the problem at each transmission slot, we first design a graph called herein the D2D Rate-Aware IDNC graph where its vertices have weights that judiciously balance between the rates/powers of the transmitting UDs and the number of their scheduled UDs. Then, we propose an innovative and efficient CLNC solution that iteratively selects a set of transmitting UDs only if the interference caused by the transmissions of the newly selected UDs does not significantly impact the overall completion time. Simulation results show that the proposed solution offers significant completion time reduction compared with the existing algorithms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".