IEEE Access Special Section: Advances in Interference Mitigation Techniques for Device-to-Device Communications
Bibliographic record
Abstract
The emergence of data-extensive applications such as online gaming and video sharing has resulted in an exponential increase in mobile data traffic. Advances in the Internet-of-Things (IoT) and 5G also require the fusion of multiple sensors and wireless devices operating in real time. Supporting such high data rate demands within the framework of existing wireless access networks is a challenging task. Addressing this ever-increasing demand of data-hungry devices in an efficient and effective manner has driven the wireless industry to look into new paradigms. Device-to-Device (D2D) and Machine-to-Machine (M2M) communications are viewed as promising solutions to this complex problem and hence a key enabling technology for 5G and IoT applications. The D2D is envisioned to operate either in out-band mode, representing use of a dedicated spectrum, or in-band mode, representing operation within the same spectrum of the existing cellular spectrum. Variations of Vehicle-to-Vehicle (V2V) and Human-to-Human (H2H) are used to deal with the specific nature and application of the wireless network under consideration.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.018 |
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".