Bibliographic record
Abstract
Device-to-Device (D2D) communication underlying cellular networks was first introduced in the 3GPP Rel. 12 specifications, and was initially referred to as proximity services (ProSe). Its primary aim was to serve billions of Internet of Things (IoT) devices for 5G and beyond-5G networks. To enable D2D link establishment between various user equipments (UEs), the neighbor discovery process became crucial. This article investigates neighbor discovery for ProSe and Vehicle-to-Everything (V2X) communications through a SideLink interface, which is specifically introduced to support D2D communications over cellular networks. A single-user scenario is first considered to derive the probability of discovery in its closed-form and compare it with simulation results to validate its theoretical analysis. This scenario employs the demodulation reference signal (DMRS), where a power-normalized-correlation (PNC)-based metric is performed to determine the presence of active peers in the vicinity. Moreover, a multiuser scenario is considered to assess the impact of interference on the discovery probability in the cases of low and high vehicular mobility channel models. Then, group discovery is investigated using two strategies: 1) a distributed scheme incorporating out-of-coverage communications (modes 2 and 4) or 2) a network-assisted scheme applicable for supervised communications (modes 1 and 3). In this study, discovery periods are modeled as an Aloha-like protocol in the first case and a Polling-like protocol in the second case with either MAC layer or PHY layer collision models. Simulations are performed to evaluate the time required for group discovery completion as well as the collision rate in both low-mobility and high-mobility channels.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".