Network Coding as a Performance Booster for Concurrent Multi-Path Transfer of Data in Multi-Hop Wireless Networks
Bibliographic record
Abstract
The emerging use of multi-homed wireless devices along with simultaneous multi-path data transfer offers tremendous potentials to improve the capacity of multi-hop wireless networks. The use of simultaneous data transfer over separate disjoint paths in multi-hop wireless networks to increase network capacity is a less explored subject, mainly because of the challenges it triggers for the reliable transport layer protocols. Reliable transport layer protocols generally use packet sequence number as a mean to ensure delivery. As such, the out-of-order packet arrival in reliable transport layer protocols triggers receiver buffer blocking that causes throughput degradation and prevents the reliable multi-path transport layer protocol to realize its vast potential. This paper focuses on integrating network coding with a reliable multi-path transport layer protocol to resolve the receiver buffer blocking problem. We propose an adaptive network coding mechanism to desensitize the receiver against packet reordering and consequently eliminate the receiver buffer blocking problem. Our state-of-the-art network coding scheme uses a combination of Q-learning and logistic regression for rare data events to control the number of redundant packets based on the network dynamics. We confirmed the veracity of our proposed scheme by a queuing theory based mathematical model. Moreover, the effectiveness of the proposed scheme is demonstrated through simulations and testbed experiments.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".