Weighted partial network coding and its applications in wireless mesh networks
Bibliographic record
Abstract
ABSTRACT Network coding (NC) has showed to be beneficial to improve transmission performance in wireless mesh networks. Random linear coding is usually applied as the default coding schema. However, random linear coding causes significant decoding delay and jitter at receiver. Further, current NC does not support weight assignment to original packets, which is however indispensable for popular applications such as quality of service control and multipath media streaming in wireless mesh networks. Partial network coding (PNC) can largely reduce decoding delay and receiving fluctuation while keeping the benefit of NC. However, PNC does not support weight‐based data replacement and weight assignment to original packets. In this work, we propose weighted partial network coding (WPNC), which is a generalized coding schema of PNC. WPNC inherits all merits of PNC and part of NC. With WPNC, both decoding delay and receiving fluctuation will be reduced as observed in PNC. Also, WPNC is quite suitable for those applications that require weight assignment to original packets. After providing the whole framework of WPNC and thorough theoretical analysis to its performance, we have demonstrated how WPNC can be integrated with quality of service control and multipath routing supported media streaming in wireless mesh networks. Performance of WPNC is inter‐validated by both theoretical analysis and numeric evaluations. Copyright © 2011 John; Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".