Predictive sliding‐mode congestion control for wireless access networks with singular and non‐singular control gain
Bibliographic record
Abstract
Transmission control protocol, in the transport layer of a network, can usually detect congestion after its occurrence. Therefore, designing a robust active queue management (RAQM) is imperative to prevent congestion along with being robust against wireless environment issues such as packet error rate and fading. Moreover, a communication network suffers from input delay as well as the state delay which is multiplied to the control input signal. The main contribution of the authors’ study is to design a predictive sliding mode control (PSMC) procedure as a RAQM to guarantee the input delay system stability and to regulate the queue length to the desired value. Firstly, a predictor is designed for the original system to obtain an input delay free model. Then, a RAQM is designed based on PSMC for the system with non‐singular and singular control gain. The disturbance observer ensures that the estimation error tends to zero. Unlike the prevalent procedures designed for the networks, the proposed method can avoid the singular gain problem in the control design. Furthermore, it can stabilise the system and can prevent congestion with robustness against external disturbances. Finally, the simulation results, obtained from SIMULINK and professional network simulator 2, confirm the analytical consequences.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".