Optimizing Throughput of UWB Networks with AMC, DRP, and Dly-ACK
Bibliographic record
Abstract
In wireless networks, the physical layer adaptive modulation and coding (AMC) scheme has been proposed to improve bandwidth efficiency over the time-varying channel. In this paper, we study the performance of ultra-wideband (UWB) based wireless personal area network where AMC is coupled with the distributed reservation protocol (DRP) and the delayed- acknowledgement (Dly-ACK) schemes at the link layer. Considering the channel variation caused by the people shadowing effect, we first propose an analytical model using an embedded Markov chain to investigate the queuing behavior at sender's buffer. Second, the throughput optimization problem is formulated and the optimal transmission mode and payload length are obtained. Simulation results are given to validate the analysis. By jointly considering channel characteristics, physical layer and link layer transmission schemes, the analytical results of the paper can provide useful guidelines for cross-layer optimization, which is essential to ensure quality of services in UWB networks.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".