Stable Queue Management for Supporting TCP Flows over Wireless Networks
Bibliographic record
Abstract
Congestion control for wireless networks is much more challenging than that for wired networks, due to the limited wireless spectrum and severe impairments of wireless medium which suffer time-varying fading, shadowing, interference, etc. Although the stability of the Internet using TCP congestion control and active queue management (AQM) schemes has been extensively investigated, effective congestion control for wireless networks is a pressing, open issue. Considering the dynamics of wireless links, in this paper, we investigate the stability of TCP/AQM wireless networks with feedback delays, which is formulated as a delay Markov jump linear system (DMJLS). First, a dynamic model based on the DMJLS for TCP/AQM wireless networks is established. Second, a novel stochastic stability analysis for autonomous time-delay systems with a cost function is presented. Delay-dependent linear matrix inequalities (LMIs) criteria for the stochastic stability conditions are obtained. It is noteworthy that this is the first time conditions for the stochastic stability have been derived subject to the linear quadratic (LQ) control strategy, and packet drop probability as the control input under the DMJLS is calculated. In addition, for practical systems where real-time tracking of states is infeasible or costly, the mode-independent congestion control is also investigated. The robustness of random early detection (RED) in wireless environment is proved. Numerical results are given to validate the analytical results which provide important insights for wireless network congestion control and resource management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".