An Evolutionary Approach for Multiple Flying Base Stations Deployment in NOMA-Based Networks
Bibliographic record
Abstract
This paper addresses the problem of deploying a Non-Terrestrial Network (NTN) using multiple Flying Base Stations (FBSs) to serve multiple Stationary Ground Users (SGUs). Our objectives are to minimize the number of active FBSs, reduce overlapping coverage areas, and increase data rates for SGUs. We formulate this as a multi-objective, non-convex Non-Linear Mixed Integer Programming (NLMIP) problem, incorporating both deployment and resource allocation practical constraints. To manage the problem's complexity, we decompose it into two sub-problems: FBS deployment and resource allocation optimization. To optimize the 2D deployment of FBSs in horizontal space, we propose a general, low-complexity heuristic method based on a statistical variant of the Self-Organizing Map (SOM) algorithm, named Evolutionary Bayesian SOM (EBSOM). Then, utilizing a Non-Orthogonal Multiple Access (NOMA) communication scheme between FBSs and SGUs, we maximize the downlink sum rate while adhering to constraints on FBS positions, transmission power, and bandwidth availability. In this stage, the deployment of FBSs is further refined to enhance resource allocation by dividing the problem into two non-convex sub-problems, which are solved iteratively using Surrogate Optimization (SO). Numerical simulations demonstrate the effectiveness of our strategy, particularly in reducing the number of active FBSs, minimizing overlapping areas, and improving network sum rates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".