Performance of cache placement using supervised learning techniques in mobile edge networks
Bibliographic record
Abstract
<p>With the growth of mobile data traffic in wireless networks, caches are used to bring data closer to mobile users and to minimise the traffic load on macro base station (MBS). Storing data in caches on user terminals (UTs) and small base stations (SBSs) faces challenges with respect to the decision of cache contents. Here, a multi‐objective cache content strategy that aims to maximise the cache hitrate of SBSs in mobile edge networks (MENs) is proposed. The multi‐objective cache placement optimisation is formulated as a classification problem. Unlike previous work, mobility input attributes such as user locations, contact duration, communication ranges, contact probability between UTs and SBSs, etc. as well as content popularity and the correlation between these input attributes separating the decision space into two regions of cache and not cache are used.Stochastic gradient descent algorithm is used for the training of three supervised machine learning techniques: artificial neural network ANN, support vector machine (SVM), and logistic regression LR to define the hyperplane that separates the cache content decision space. Simulation results show that compared with the weighted‐sum approach, the SBSs cache hit rates increase on the average by 18.58%, 18.52%, and 18.2%, and the total energy consumption values decrease on the average by 33.49%, 53.19%, and 49.9% for ANN, SVM, and LR, respectively.</p>
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 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 | 0.001 | 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 | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".