Intelligent Cache-Assisted Mobile Edge Computing via Deep Learning
Bibliographic record
Abstract
Mobile edge computing (MEC) and caching stand out as pivotal technologies for the future of wireless networks. Efficiently anticipating users’ demands holds paramount importance in meeting the surge in user requests. Leveraging the high prediction accuracy of deep learning (DL) and the recent enhancements in computational capabilities, DL algorithms are seamlessly integrated into wireless systems. Hence, in this letter, in a wireless heterogeneous environment comprising multiple small base stations (SBS) linked to a central base station (BS), we propose a joint integration of K-means clustering and DL framework to predict popular contents. The central BS collects the information from all the SBSs to learn the data. Then, the popularity rank for each content is obtained, and the contents are cached in real-time. Tensorflow and Keras libraries are used for the prediction model on the MovieLens dataset. Simulation results are provided to show that the proposed model significantly outperforms the recent prediction models in terms of average cache hit rate and mean squared error.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".