Machine Learning System for Predicting Latency in Next-Generation Wireless Network
Bibliographic record
Abstract
The upcoming wireless networks 5G and 6G need extremely short delays to support essential applications such as autonomous systems together with real-time voice and data communications. The precise prediction of latency needs to be established for wireless networks to optimize resources and improve user satisfaction. The research develops a machine learning prediction system for wireless networks' latency in heterogeneous environments through Random Forest and XGBoost and Long Short-Term Memory (LSTM) network implementations. The predictive system integrates signal-to- noise ratio (SNR) along with bandwidth into its analysis besides using user mobility parameters and base station loading metrics and channel environmental data. The LSTM model demonstrated excellent results during testing of simulated and genuine datasets along with reaching an R2score of 0.93 and a Mean Absolute Error (MAE) of 1.83 ms above traditional regression models. The research findings show that the model successfully predicts latency measurements using precise accuracy across different network operational conditions. This anticipatory system creates a flexible real-time scheduling solution for 5G and 6G networks which controls ultra-reliable low-latency communication (URLLC) applications efficiently.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".