Profit Maximization in 5G+ Networks with Heterogeneous Aerial and Ground Base Stations
Bibliographic record
Abstract
In this paper, we propose a novel framework for 5G and beyond (5G+) heterogeneous wireless networks consisting of macro aerial base stations (MABSs), small aerial base stations (SABSs), and ground base stations (GBSs) with two types of access technologies: power domain non-orthogonal multiple access (PD-NOMA) and orthogonal frequency-division multiple access (OFDMA). We aim to maximize the total network profit under some practical network constraints, e.g., NOMA and OFDMA limitations, transmit power (TP) maximum limits, and isolation of the virtualized wireless network. We formulate the resource allocation problem encompassing joint TP allocation, ABS altitude determination, user association, and sub-carrier allocation parameters. Our optimization problem is mixed integer non-linear programming (MINLP) with high computational complexity. To propose a practical approach with reduced computational complexity, we use an alternate method where the main optimization is broken down into three sub-problems with lower computational complexity. We do this by adopting successive convex approximation (SCA), geometric programming (GP), and mesh adaptive direct search (MADS) to solve each of the resulting problems, and find power allocation, altitudes of ABSs, and assignment parameters, respectively. Simulation results reveal that our proposed scenario can improve the overall network profit by up to 47 percent compared to the case where the TPs and ABS altitudes are fixed. Besides, finding the ABS altitude with fixed TPs can improve the network profit by 20 percent compared to the power allocation case with fixed ABS altitudes. Our proposed heterogeneous approach improves the network profit by up to 18, 16, 15, and 10 percent in suburban, urban, dense urban, and high-rise urban environments, respectively, compared to the cases with homogeneous ABSs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".