Bibliographic record
Abstract
Downloading remote files (e.g., pictures, videos) from online social networks via smart user equipments (UEs) (e.g., smartphones, tablets) is becoming popular. Friends who are nearby may want to download the same files shared by their mutual acquaintance. People can obtain these files in a device-to-device (D2D) manner via opportunistic connections to reduce their payment for data service. This is referred to as D2D data offloading. However, D2D communications on unlicensed spectrum using Bluetooth or WiFi-Direct may not maintain high data rate when many D2D pairs nearby need to communicate simultaneously. Since D2D connections are transient, it is important to improve spatial reuse of communication resources and increase the data rate of opportunistic D2D communications. In this paper, we propose a scheme to reuse the downlink licensed spectrum of cellular networks for D2D data offloading. Our proposed scheme includes determining the availability of digital files on neighbouring devices, estimating the channel gains, and performing channel allocation and power control for D2D pairs. Simulation results show that our proposed scheme does not affect the existing cellular UEs and it can also offload more data traffic when compared with WiFi-Direct on an unlicensed spectrum.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".