Deep Learning-Empowered Framework for Performance Prediction in IEEE 802.11ah-Based Networks
Bibliographic record
Abstract
With the rapid advancement of information technologies, supporting massive Internet of Things (IoT) connections remains a significant challenge for wireless communication systems. The IEEE 802.11ah standard introduces the restricted access window (RAW) mechanism to reduce station (STA) collisions by grouping STAs into separate RAW groups. In dynamic IEEE 802.11ah-based environments, accurately predicting RAW performance is particularly challenging. To address this issue, this paper proposes a bidirectional long short-term memory (BiLSTM) deep learning framework for predicting network performance. Simulation results demonstrate that the Bi-LSTM model achieves superior prediction accuracy in terms of normalized mean squared error. Compared to the multilayer perceptron model, the Bi-LSTM reduces throughput error by $\mathbf{5 4. 5 \%}$, delay prediction error by $42 \%$, and collision probability prediction error by $65.8 \%$.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".