Optimal Data Transmission and Channel Code Rate Allocation in Multipath Wireless Networks
Bibliographic record
Abstract
Wireless links are often unreliable and prone to transmission error, particularly when network users are mobile. These can degrade the performance in wireless networks, particularly for applications with tight quality-of-service requirements. A common remedy to this problem is channel coding. However, this per-link solution can compromise the link data rate, leading to an undesired end-to-end performance. In this paper, we show that this shortcoming can be mitigated if the end-to-end transmission rates and channel code rates are properly selected over multiple routing paths. We formulate a joint channel coding and end-to-end data rate allocation problem in multipath wireless networks with max-min fairness as the objective function. Our goal is to maximize the minimum throughput available among the network users. To cope with the fast and frequent changes in dynamic environments that are typical for vehicular networks, we address both adaptive and nonadaptive channel coding scenarios. Unlike similar formulations in single-path routing networks, in the multipath routing case, we face an optimization problem that is nonconvex and usually difficult to solve. We tackle the nonconvexity by using function approximation and iterative techniques from signomial programming. Simulation results confirm that by using channel coding jointly with multipath routing, we can significantly improve the end-to-end network performance compared with the case when only one of them is used in the network. Nonadaptive channel coding is also shown to achieve a high degree of optimality with much less complexity.
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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 | 0.000 | 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".