Exploring spatial reuse effects on performance enhancements in wireless multihop networks
Bibliographic record
Abstract
Recent years have witnessed a remarkable interest in wireless multihop ad hoc networks that need little or no infrastructure support. Such networks have enabled the existence of various applications ranging from the monitoring of herds of animals to supporting communication in military battle-fields and civilian disaster recovery scenarios as well as providing an emergency warning system for vehicles on the road. Currently, the distributed coordination function (DCF) of the IEEE is the industry dominant MAC protocol for wireless multihop ad hoc environment due to its simple implementation and distributed nature. Nevertheless, the DCF access method does not make efficient use of the shared channel due to its inherent conservative approach in assessing the level of interference. Moreover, the implementation of DCF in multihop ad hoc networks suffers from the exposed and hidden terminal problems; both of these problems highly affect the spectrum spatial reuse and accordingly causes serious throughput deterioration. To date, various methods have been proposed to enhance the throughput of the DCF; namely, tuning the carrier sensing threshold, the transmission attempt probability through changing the binary exponential backoff, controlling the frame transmit power, adapting the physical transmission rate of data frame, and the use of directional antennas. In this thesis, we develop mathematical tools to study the effectiveness of the interplay among the various tunable parameters and propose suitable protocols for achieving better utilization of the wireless spectrum.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".