Delay Analysis of Distributed Reservation Protocol with UWB Shadowing Channel for WPAN
Bibliographic record
Abstract
Ultra-wideband (UWB) technology is expected to provide high data rate services for future wireless personal area networks (WPANs). The WiMedia Alliance recently has launched its standard for UWB-based WPANs, where the distributed reservation protocol (DRP) is specified to allow the channel time being reserved in a distributed manner. In view of the urgent need of using DRP to support high data rate multimedia applications, we investigate the delay performance of DRP in this paper. Since the negotiation of channel time is fully distributed without centralized coordination, the reserved channel time may be non-evenly spaced. In addition, the channel dynamics due to shadowing that is notable in indoor environments can greatly affect the protocol performance. In this paper, we study the delay performance of DRP under different reservation patterns and take into account the dynamics of UWB shadowing channel. The system is modeled as a discrete-time single server queue with vacation, which can be represented by the quasi-birth and death (QBD) process and solved by the well-established matrix-geometric approach. We use numerical results to validate the accuracy of the mathematical modeling. The proposed analytical model can be useful to understand the actual performance of DRP, thereby further performance improvement can be guided.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".