Enhancing Traffic Load Forecasting in 5G Networks: A Statistical and Temporal Feature Engineering Approach
Bibliographic record
Abstract
The rapid advancement of 5G technology has significantly increased energy consumption, underscoring the need for advanced energy management solutions. Proactive energy management, which relies on accurate predictions of network load to enable timely adaptive actions, emerges as a key strategy in addressing this challenge. In this study, we introduce a refined approach to forecasting traffic load in 5G networks, emphasizing the integration of statistical and temporal feature engineering. This methodology is aimed at capturing the intricate spatial and temporal patterns inherent in network data, thereby enhancing prediction accuracy. Leveraging an existing dataset comprising measurements from 1,000 base stations, we enriched this dataset with a set of derived features that reflect both temporal dynamics and load characteristics. Utilizing this enriched dataset, we trained and validated a suite of predictive models. Our findings reveal a notable improvement in the accuracy of traffic load predictions, outperforming standard baseline models. This underscores the effectiveness of our feature engineering approach in refining the predictive capabilities of models, paving the way for more efficient and proactive energy management in 5G networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 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".