A Dynamic Bernstein Graph Recurrent Network for Wireless Cellular Traffic Prediction
Bibliographic record
Abstract
Predictive analysis of wireless cellular traffic plays an important role in network resources provisioning. Accurate traffic prediction is a challenging task due to the dynamic spatial-temporal nature of wireless traffic. Most of the existing approaches do not consider spectral domain information for wireless traffic prediction. Some of the approaches cannot capture the spatial dependencies between neighbouring and distant cells. In this paper, we propose a dynamic Bernstein graph recurrent network for traffic prediction in wireless cellular networks. First, we design a spectral dynamic graph construction (SDGC) method to model the spatial dependencies between cells as a dependency graph in a data-driven fashion. A dynamic Bernstein polynomial filtering (DBPF) scheme based on the$K$-order Bernstein polynomial approximation is then developed to capture the spatial correlations and infer the cell-specific parameters. To predict the spatial-temporal traffic demands, we propose a dynamic Bernstein graph recurrent network (DBGRN), which integrates the proposed DBPF module with a gated recurrent unit (GRU) network. We evaluate the performance of our proposed model using a real-world dataset. Results show that our proposed model outperforms four state-of-the-art baseline schemes, and achieves up to 8% and 10% improvements in terms of the root mean squared error (RMSE) and mean absolute error (MAE), respectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".