Bibliographic record
Abstract
Nowadays it is very popular to share video clips and images to one’s social network in the proximity. Direct device-to-device (D2D) communication is one of the means to respond to this requirement. D2D offers users improved end-to-end latency times, and additionally can provide higher data rates. At the same time the overall cellular network congestion decreases. D2D is also known as Proximity Services (ProSe). LTE is missing direct D2D communication. Currently D2D for 5G is standardised in the 3rd Generation Partnership Project (3GPP) Releases 12, and in parallel Mobile and wireless communications Enablers for the Twenty-twenty Information Society (METIS) project has D2D as one of its research topics. Multiple articles have been published about D2D communication. This thesis is a literature based thesis following D2D communication in 5G literature. The scope is to describe similarities and differences found in Technical Reports and Technical Specifications of the 3GPP Release 12, in deliverables written in METIS project and in some selected D2D related publications about D2D communications. 3GPP Release 12 concentrates on ProSe at least for public safety. ProSe communication out-of-coverage is only for public safety purposes. METIS provides multiple solutions for diverse D2D topics, for example, device discovery, radio resource management, mobility management and relaying. METIS provides solutions for D2D communication not yet mature enough for development and implementation but which might be realized in the future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".