An enhanced cooperative MAC protocol based on perceptron training
Bibliographic record
Abstract
Cooperation among wireless nodes at the medium access control (MAC) layer has attracted a lot of research attention in recent years. Most of existing cooperative MAC protocols focus on the scenarios with static helpers (relay nodes). However, when the helpers are moving around, the source node may choose a leaving helper with out-of-date information, which could cause performance deterioration. Hence, an optimal helper should not only support a high transmission rate but also have a low mobility. It can be a challenging problem to distinguish such an optimal helper when there are moving helpers of various mobility. In this paper, we extend the cooperative MAC protocol in [1] by means of perceptron training, referred to as PTCoopMAC. Making use of the handshaking messages in the original CoopMAC protocol, PTCoopMAC collects history data on the signal strength of overheard packets. Then, PTCoopMAC applies the perceptron training technique to obtain a weight vector to examine the stability of the helpers. Extending the CoopTable, PTCoopMAC selects the optimal helper depending on the achievable data rate as well as the prediction on whether a helper is reliable. The simulations results well demonstrate the throughput improvement of PTCoopMAC and its robustness to high mobility of helper nodes.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".