Graph Neural Network-Based Internet Traffic Prediction in 6G Networks with Genetic Algorithm Hyperparameter Optimization
Why this work is in the frame
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Bibliographic record
Abstract
Accurate internet traffic prediction is a key challenge in managing next-generation networks such as 6G. This paper presents a novel approach based on Graph Neural Networks (GNNs) for predicting internet traffic in 6G networks. The proposed model integrates Graph Attention Networks (GAT) and Transformer architectures to learn spatial and temporal dependencies in traffic data. A K-Nearest Neighbors (KNN)-based graph construction method is utilized to represent spatial relationships between network cells. The model’s performance is enhanced by leveraging a Genetic Algorithm (GA) for hyperparameter optimization. Experimental results demonstrate the effectiveness of the proposed model in achieving superior prediction accuracy, as evidenced by improvements in RMSE, and MAE compared to baseline models. This work offers a scalable solution for traffic prediction in 6G networks.
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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 | 0.000 | 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 it