Online Traffic Prediction in Multi-RAT Heterogeneous Network: A User-Cybertwin Asynchronous Learning Approach
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Bibliographic record
Abstract
In this paper, we propose a novel traffic prediction scheme for multiple radio access technology (multi-RAT) heterogeneous network. The scheme is named user-Cybertwin asynchronous learning (UCAL), which aims to extract meaningful patterns from noisy network traffic measurements and mitigate the impact of highly nonstationary measurements for ensuring the prediction accuracy. Specifically, we design a pattern extraction method that minimizes the Frobnius norm between the collected measurements and the expected k-rank approximation of the measurements in order to extract useful information. Then, by transforming the conventional long short term memory (LSTM) model into a nonlinear state space and incorporating Gaussian noise, we develop an online LSTM algorithm to adapt fast to changing environments. As a result, the parameter updating of the new online LSTM model can keep up with data changes while capturing complicated and nonlinear relationships among measurements. We consider both the surrounding environment conditions on the mobile user side and end-to-end link conditions on the Cybertwin side, and iteratively update the model parameters in both Cybertwin and MU. Simulation results demonstrate that the proposed UCAL scheme can achieve high traffic prediction accuracy in comparison to existing schemes. It can also significantly improve the efficiency in maintaining the prediction accuracy even when the dimension of traffic measurements increases.
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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 | 0.001 |
| 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 it