IEEE 802.15.3 intra-piconet route optimization with application awareness and multi-rate carriers
Bibliographic record
Abstract
In IEEE 802.15.3 wireless personal area networks, all devices (DEVs) within a piconet communicate in a peer-to-peer manner. In this paper, we enhance the performance of intra-piconet communications by taking advantage of the multi-rate physical layer (PHY) and diverse application traffic characteristics. As higher PHY data rates are typically achievable over shorter transmission distances, a low rate link between two DEVs can potentially be replaced by a higher rate multihop connection through intermediate DEVs in the same piconet. A novel application-aware shortest path (AASP) algorithm is proposed for centralized intra-piconet route optimization, which finds the route that requires the minimum overall channel time allocation (CTA) based on the traffic parameters. Performance evaluations show that the effective CTA rates vary dramatically for different applications, and can be greatly increased by simply delaying the acknowledgments. Especially for low rate links and between unreachable DEV pairs, the AASP algorithm yields very high optimization ratios, which increase with frame payload size and the density of DEVs in the piconet.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".