Bayesian Meta-Learning for Adaptive Traffic Prediction in Wireless Networks
Bibliographic record
Abstract
Wireless traffic prediction is indispensable for network planning and resource management. Due to different population distributions and user behavior, there exist strong spatial-temporal variations in wireless traffic across different regions. Most of the conventional traffic prediction approaches can only tackle a particular spatial-temporal pattern and cannot capture such variations in wireless traffic. This motivates us to develop an adaptive approach which can tackle spatial-temporal variations and predict wireless traffic in different regions. In this paper, we formulate an adaptive traffic prediction problem from a probabilistic inference perspective and develop a variational spatial-temporal Bayesian meta-learning (VST-BML) algorithm. We model the traffic prediction in different regions as different prediction tasks. The proposed VST-BML algorithm can learn the common spatial-temporal features shared by all prediction tasks, and adaptively infer the task-specific parameters to tackle spatial-temporal variations. We evaluate the performance of our proposed VST-BML algorithm using a real-world traffic dataset. Experimental results show that the proposed algorithm can quickly adapt to different prediction tasks by using only a small number of data samples and provide accurate traffic prediction in different regions. When compared with five baseline methods, the proposed algorithm can reduce the root- mean-square error (RMSE) and mean absolute error (MAE) by 53.0% and 48.4%, 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".