Packet interference in a heterogeneous cluster of Bluetooth piconets
Bibliographic record
Abstract
In a Bluetooth piconet, the master essentially controls the channel. Due to an absence of coordination between independent masters while accessing the wireless medium, devices will encounter high packet interference if several piconets are simultaneously operating in the same area. Since even a headset and a mobile phone can be connected with a Bluetooth link, forming a piconet, it may not be unusual to find tens of independent piconets in crowded places like airports, international conferences, shopping malls, and so on. The study of packet interference gains importance, because it affects throughput of a piconet. Considering that all the portable devices can have a Bluetooth interface and people are highly mobile these days, it is not uncommon to find a cluster of piconets of both the 79-hop and the 23-hop types in the same area. In this paper, we present an analytical model of packet interference in a heterogeneous cluster of Bluetooth masters. By a heterogeneous cluster, we mean a cluster of piconets consisting of 23-hop and 79-hop types. Our analytic model is based on the idea of probabilistic graphs, where a node denotes a piconet and an edge denotes the probability of interference between two nodes.
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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".