A token-based connectivity update scheme for unmanned aerial vehicle ad hoc networks
Bibliographic record
Abstract
Code division multiple access mobile ad hoc networks (CDMA-MANETs) are envisioned to be the next-generation networking architecture for networking military platforms in a battlefield. In this paper we consider a CDMA ad hoc network consisting of multiple unmanned aerial vehicles (UAVs). We propose a token-based connectivity update scheme to solve the code collision problem in assigning code channels as well as the network link update problem for the CDMA UAV ad hoc network. Our proposed scheme uses a token message, which continuously circulates around the network in a non-predetermined order, to conduct assignment of code channels for each UAV. By using the broadcast properties of the wireless communication media, our proposed scheme is able to discover new or lost neighbors almost in real time. Moreover, the proposed token-based connectivity update scheme implements spatial reuse of code channels, which is mandatory in large-scale ad hoc networks due to the limited size of the CDMA code set. We then derive a theoretical result for approximating the connectivity update latency, which is further verified through computer simulations.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".