Bibliographic record
Abstract
One of the reasons that cause low transmission throughput in IEEE 802.11-based multihop networks is packet losses. Compared to transmitting a new packet, retransmitting a packet in 802.11-based networks has a lower priority in accessing the channel and requires a longer channel idle time for backoffs. When the number of retransmissions exceeds a certain threshold, a packet is dropped at the link layer. For TCP traffic, this will eventually result in TCP timeout, and the lost packet will be retransmitted at the transport layer, causing end- to-end throughput degradation. Furthermore, the mechanism that TCP adjusts its congestion window size negatively affects the transmission throughput in 802.11-based multihop networks. In this paper we propose an opportunistic link scheduling (OLS) protocol, which schedules transmissions of the links based on their channel conditions, including both channel fading and co-channel interference. Links with good channel conditions are given a higher priority to access the channel and allowed to transmit a limited number of packets consecutively without repeatedly competing the channel. OLS also includes mechanisms to avoid buffer overflow and prevent starving links with poor channel conditions. Our results show that OLS can significantly improve the end-to-end transmission throughput, while keeping reasonably low transmission delay. The protocol is easy to implement, and requires minor changes to the 802.11 protocol.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".