Optimal Cooperative Relaying Schemes in IR-UWB Networks
Bibliographic record
Abstract
Cooperation between wireless nodes to retransmit data for the other users introduces multiuser diversity to a wireless network and increases the system throughput. In this paper, the optimal cooperative relaying strategies in the MAC layer are analyzed while considering the UWB unique properties such as fine ranging and immunity to small scale fading. Specifically, the optimal cooperation strategies in the absence of coordination message passing between relays are determined in order to maximize the system throughput while reducing the control packet overhead. Mobile networks are also considered, in which the relays should exchange their ranging information together in some update intervals. The optimal update interval length is calculated in order to maximize the system throughput. More importantly, we show that if this optimal update interval is used, the optimal cooperation strategies in the mobile case will be similar to those in the static network. Two different relay selection schemes, namely proactive and reactive settings, are considered. Analysis and simulations confirm that the proposed UWB-based Cooperative Relaying Scheme, UCoRS, can achieve a considerable diversity gain in spite of its implementation simplicity. UCoRS also minimizes the number of control packets that are required for the optimal cooperation, which leads to the energy efficiency in the UWB costly data-receiving process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".