Adaptive fractional order predictive sliding mode control for congestion control of wireless access networks
Bibliographic record
Abstract
Abstract This article studies congestion control of wireless access networks. In a wireless access network, it is necessary to design a robust active queue management (RAQM) technique to control congestion occurrence and to make the network robust simultaneously against some issues in the wireless link aspects, such as packet error rate (PER) and fading effects. On the other hand, a network is often described by a nonlinear model, in which the delayed packet drop probability is assumed as the input signal. So in this article, a RAQM technique is proposed based on an adaptive fractional order predictive sliding mode control (AFOPSMC) method, through which the following are achieved: (1) the stability of the nonlinear system with input delay is assured, (2) the congestion occurrence is prevented by controlling the queue measurement to the desired value, (3) the robustness against the external disturbances is achieved, and (4) the input signal obtained by the controller is limited between 0 and 1. In contrast to most recently published papers where input delay is ignored in the system description and input saturation is achieved through designing parameters, this article at first proposes a predictor to eliminate the input delay and then designs a compensated system to deal with the input signal constraint. Furthermore, the chattering phenomena, as a commonly caused issue in the sliding mode control, is eliminated based on the adaptive laws designed for the controller parameters. The theoretical results are validated and compared with some other related protocols through numerical simulations using Simulink and professional network simulator 2 (NS2).
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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 | 0.000 |
| 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".