Using Machine Learning to Locate Gateways in the Wireless Backhaul of 5G Ultra-Dense Networks
Bibliographic record
Abstract
A distributed wireless backhaul has emerged as an attractive solution for forwarding traffic to the core in 5G Ultra-Dense Networks (UDNs). It consists of a large number of small cells and a few of these cells, referred to as gateways, are linked to the core by high capacity fiber optic links. Each small cell is associated to one gateway and forwards its traffic to it directly or through multiple hops. The backhaul network capacity increases by decreasing the average number of hops. In this paper, we consider two machine learning-based clustering algorithms, namely, k-means and k-medoids, to find gateway locations that minimize the average number of hops. We compare their performance with a baseline approach at different small cell densities through extensive Monte Carlo simulations in terms of average number of hops. The results indicate that both clustering algorithms significantly outperform the baseline approach and k-medoids performs equal to or better than k-means.
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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 | 0.000 | 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 | 0.000 | 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 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".