On the Design of Large-Scale Cellular Mobile Networks Using Tabu Search
Bibliographic record
Abstract
In this paper, we proposed a tabu search approach to design large-scale UMTS mobile networks and to specifically solve the problem of assigning Node Bs to RNCs in cellular mobile networks. Experiments were conducted to measure the quality of solutions provided by this algorithm. This approach was compared against genetic algorithm and simulated annealing. Computational results obtained confirm the efficiency and the effectiveness of the tabu search to provide better solutions than genetic algorithm and simulated annealing, especially for large-scale cellular mobile networks with a number of Node Bs varying between 100 and 400, and a number of RNCs oscillating between 5 and 8, meaning that the search space size ranges between 5100 and 8400 and that the average improvement rates are in the order of 2% and 7% respectively. This improvement represents a substantial reduction in maintenance and operations costs, which, for a 5 year period, amount to millions of dollars.
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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 | 0.002 | 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 | 0.004 | 0.002 |
| Research integrity | 0.000 | 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 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".