TCP-Aware Network Coding with Opportunistic Scheduling in Wireless Mobile Ad Hoc Networks
Bibliographic record
Abstract
In this paper, we present a scheme that employs TCP aware network coding with opportunistic scheduling to enhance TCP performance in wireless mobile ad hoc networks. Specifically, we consider a TCP parameter, congestion window size, and wireless channel conditions simultaneously to improve TCP throughput performance. We evaluate our scheme by using ns2 simulations in which the mobility and the traffic parameters are varied. The results show that our scheme gives approximately 35% performance improvement in a high mobility environment and about 33% performance improvement in no/low mobility environment as compared to traditional network coding with opportunistic scheduling. The results further show that when more TCP sessions are generated in the network, our scheme performance increases by approximately 6.9 Kbps per TCP session as compared to traditional network coding with opportunistic scheduling which only increases by roughly 5.9 Kbps per TCP session.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".