Bibliographic record
Abstract
In 5G Ultra-Dense Cells, a distributed wireless backhaul is an attractive solution for forwarding traffic to the core.The macro-cell coverage area is divided into many small cells.A few of these cells are designated as gateways, which are linked to the core by high capacity fiber optic links.Each small cell is associated with one gateway and all small cells forward their traffic to their respective gateway through multi-hop mesh networks.In this thesis, we investigate the Gateway Location Problem and show that finding near optimal gateway locations improves the Backhaul Network Capacity (BNC).Toward this end, the p-median problem has been formulated as Integer Linear Program to find optimal gateway locations.Subsequently, we use artificial intelligence based on a Genetic Algorithm (GA) in combination with machine learning based on the K-means clustering algorithm and develop a heuristic to find near-optimal gateway locations that maximize the BNC.We evaluate the performance of our new heuristic, K-GA, in comparison with six different approaches in terms of Average Number of Hops (ANH) and BNC at different node densities through extensive Monte Carlo simulations.All approaches including the optimal (or exact) approach are tested under different small cell distribution scenarios, namely, Uniform distribution, bivariate Gaussian distribution, and Cluster distribution.K-GA provides near optimal results achieving ANH and BNC within 3% of optimal and saves on average 95% of execution time.The scheme is practical and can be easily adapted to the spatial distribution of traffic.We also analyze the effect of the number of gateways on ANH and BNC.The results show that more gateways are beneficial.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".