Probabilistic Analysis of Operating Modes in Cache-Enabled Full-Duplex D2D Networks
Bibliographic record
Abstract
Cache-enabled Device-to-Device (D2D) communications, recognized as one of the key enablers of the fifth generation (5G) cellular network, are a promising solution for reducing the great burden on mobile core networks and backhaul links. Caching, however, imposes a new networking user Key Performance Index (KPI), which is the probability of user satisfaction. In other words, how likely is it that the user will obtain the necessary information from the network? Such probability depends on the type of transmission, (i.e., half duplex or full duplex) and on many elements related to the caching system, including the way the information is cached or the popularity of the cached information. The analysis of those elements produces different modes of operation. To evaluate the new KPI, the probabilities of each mode of operation must be extracted from the transmission and caching conditions. This paper presents a thorough analysis of those probabilities, including the relevance of the relationship between caching policies, content popularity and transmission types. Such relationships allow the smooth evaluation of user satisfaction under different conditions.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".