Efficient Mobile Cellular Traffic Forecasting using Spatial-Temporal Graph Attention Networks
Bibliographic record
Abstract
Cellular traffic prediction is an essential aspect of mobile network management that uses data analytics and machine learning to forecast the volume and pattern of communication traffic generated by mobile users at a particular location and time. Graph Convolution Network (GCN) has been widely employed to model the spatial relationships between different cell towers and their neighboring counterparts. However, GCN is limited to highly regular and well-structured graphs. This paper proposes a Graph Attention Network (GAT) to capture more nuanced spatial relationships between cell towers, making it more suitable for irregular and complex graphs. Additionally, a novel graph attention mechanism is proposed that enables the creation of a dynamic graph structure, capable of capturing the evolving spatial relationships over time. Comprehensive experiments on an actual cellular traffic dataset show that the proposed technique outperforms state-of-the-art baselines on two evaluation metrics - RMSE and MAE - with a significant improvement.
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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 | 0.002 | 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 | 0.001 | 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) | 0.001 | 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 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".