Base Station Deployment Optimization in Federated Networks with Multi-Hop Communication
Why this work is in the frame
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Bibliographic record
Abstract
This paper explores a wireless base station location optimization problem and introduces a set of new constraints of interest for next-generation networks; the general strategy is similar to facility location optimization, which can be solved by linear programming. We propose solutions that relax coverage requirements and use trusted multiple hops (or relays) to reach distant devices; we give the mathematical derivation and algorithms based on the standard linear optimization formulation. We develop an optimization application and use it to conduct simulations that determine optimal base station requirements under simple full coverage scenarios, partial coverage scenarios, multi-hop scenarios, and location-constrained scenarios, which are of interest in federated networks with geographical deployment limitations. The proposed solutions are also a good option for the new 5G base station coverage optimization by using the Integrated Access and Backhaul (IAB) feature from 3GPP Release 16 (R16).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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 it