Cooperation of heterogeneous wireless networks in end-to-end congestion control for QoS provisioning
Bibliographic record
Abstract
Sharing radio resources of multiple wireless networks with overlapped coverage areas has a potential of improving the transmission throughput. However, the improvement cannot be achieved in congestion scenarios using independent congestion control procedures among the end-to-end paths. Although various network characteristics make the congestion control complex, this variety can be useful for congestion avoidance if the networks cooperate with each other. In this way, the traffic can be shifted from a congested network to non-congested ones, and the overall transmission throughput does not degrade in a congestion scenario. In this paper, first, a cooperative congestion control algorithm is proposed in which the state of an end-to-end path is provided at the destination terminal by measuring the queuing delay and estimating the congestion level. Second, the decision on when to start/stop cooperation is determined based on the network characteristics, instantaneous traffic condition, and requested quality of service (QoS). Simulation results demonstrate the throughput improvement of the proposed scheme over non-cooperative congestion control.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".