On Channel Selection Strategies for Multi-Channel MAC Protocols in Wireless Ad Hoc Networks
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Bibliographic record
Abstract
Multi-channel medium access control (MAC) protocols have recently been proposed to improve the performance of the transmission control protocol (TCP) in IEEE 802.11 wireless ad hoc networks. This paper uses ns-2 network simulations to study the impact of channel selection techniques on multi-channel MAC protocol performance, particularly for the bi-directional multi-channel MAC protocol. Three channel selection strategies are studied: random, lowest channel first, and soft channel reservation. The simulation results identify four distinct scenarios in which data channel frame losses can occur. Among the channel selection strategies evaluated, the soft channel reservation technique is the most effective for the missed reservation problem. This channel selection strategy reduces link-layer data frame losses and provides higher TCP throughput compared to the other channel selection approaches
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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