Effect of relaying on capacity improvement in wireless local area networks
Why this work is in the frame
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Bibliographic record
Abstract
Wireless relay nodes can improve the capacity of wireless networks. In this work, we integrate wireless relay nodes into the infrastructure of a wireless local area network (WLAN). In particular, we investigate the effect of different relay strategies and optimal utilization of a fixed number of immobile relay nodes, which maximizes the expected throughput capacity of the network. We study how the number of relay nodes, the range of users, transmission power, path loss exponent, and traffic characteristics affect the optimal relay node placement and expected throughput capacity of the network. Our results show that a time-division relay strategy can far outperform a receive-and-retransmit relay strategy. Furthermore, for a wide range of system parameters, optimally placed relay nodes can significantly increase the network expected throughput capacity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it