AI-based Traffic Forecasting in 5G network
Bibliographic record
Abstract
Forecasting of the telecommunication traffic is the foundation for enabling intelligent management features as cellular technologies evolve toward fifth-generation (5G) technology. In this work, a deep-learning based analysis of a traffic dataset was conducted. For this purpose, several neural network-based models are utilized. The paper explores the forecasting performance of the fully connected sequential network (FCSN). Specifically, one-dimensional convolutional neural network (1D-CNN), single shot learning LSTM (SS-LSTM), and autoregressive LSTM (AR-LSTM) models have been evaluated. In addition, the baseline model was developed to assess the performance of the aforementioned models. The results reveal that FCSN and 1D-CNN have comparable performance. However, 1D-CNN is a smaller network with less number of parameters. One of the other benefits of the proposed 1D-CNN is having less complexity and faster execution time for predicting the next 24-hour traffic.
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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 | 0.000 |
| 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 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".