Multi-rate selection and power allocation assisted probabilistic edge caching for cooperative video transmission in dense D2D networks
Bibliographic record
Abstract
In this paper, we consider a cooperative transmission model for video applications and services (VASs) in dense device-to-device (D2D) networks. The model enables the mobile users (MUs) to flexibly receive the videos from macro base station (MBS) and D2D networks with mobile edge caching. Particularly, we formulate a multi-rate selection and power allocation assisted probabilistic edge caching (MPC) optimisation problem under the resource constraints on storage, bandwidth, and power. This problem is solved for the optimal caching probabilities of requested videos corresponding to proper encoding rates selected. The optimal powers of caching MUs and MBS for transmitting the videos are also found to maximise the playback quality, while utilising the system resources. The MPC optimisation problem, which is complicated due to the presence of binary and real variables and various constraints, is feasibly solved by genetic algorithms (GA) with penalty function and truncated chromosome. Simulation results are shown to demonstrate the benefits of both GA and MPC methods compared to other benchmarks. Detailed analyses and interesting findings provide useful insights into the mobile edge caching design of dense D2D networks for VASs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".