Cooperative relaying in Wi-Fi networks with network coding
Bibliographic record
Abstract
Recently, network coding has emerged as a new cooperative technique for improving network throughputs over traditional routing techniques. Network coding allows intermediate nodes to combine packets using an algebraic function before forwarding. Selfless cooperation by the intermediate nodes is thus implicitly assumed in the network employing network coding. Such cooperation in principle should not happen for free but for mutual benefits shared by source-destination pairs and the cooperating intermediate nodes. Effective resource allocation techniques are thus required to efficiently utilize the limited network resources at the intermediate nodes. In this article, we propose one such effective buffer allocation algorithm, called buffer equalized opportunistic network coding (BE-ONC), to dynamically exploit buffer spaces at a relay node of a relay-based IEEE 802.11 network. The BE-ONC technique combines packets opportunistically based on dynamic buffer allocation at the relay. Through simulations, we illustrate that the proposed scheme improves over the classical packet scheduling schemes in terms of delay and successful packet delivery ratio in a cooperative relay-based Wi-Fi network. Our experimental results further confirm the potential benefits of BE-ONC in terms of packet delivery ratio, when compared with first-in first-out scheduling at the relay node. The guidelines for extending our proposed scheme to a more general scenario, where more than two users are involved, and the relay is allowed to transmit its own packet, are also provided.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".