Bibliographic record
Abstract
In a multihop cellular network, the physical layer of mobile terminals is modified so that in addition to being able to transmit to base stations, mobile terminals are also able to transmit directly to other mobile terminals. This allows mobile terminals to lower their maximum transmission power and use other terminals to relay their traffic towards the base station. However, there is still a large amount of interference surrounding the base station because all traffic either emanates or is destined to the base station making it the capacity bottleneck of the network. In order to reduce the interference surrounding the base station, we propose a novel architecture called the autonomous infrastructure multihop cellular network. In this architecture, certain mobile terminals that have a connection to the backbone network will be allowed to act as access points. Access points will receive traffic from other terminals and send it directly onto the backbone network, as would a base station. This will reduce the amount of traffic required to be handled by the base station and increase network capacity. The results of our analysis and simulations show that when mobile terminals can act as access points, the SINR at the base station is higher, the power consumption is lower and the coverage is better than in a normal multihop cellular network.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".