Near-Optimal Resource Allocation Algorithms for 5G+ Cellular Networks
Bibliographic record
Abstract
Fifth-generation and beyond (5G+) systems will support novel cases, and hence, require new network architecture. In this paper, network flying platforms (NFPs) as aerial hubs are considered in future 5G+ networks to provide fronthaul connectivity to small cells (SCs). We aim to find the optimal association between the NFPs and SCs to maximize the total sum rate subject for quality of service, bandwidth, and supported number of link constraints. The formulated optimization problem is an integer linear program and the optimal association between the NFPs and SCs is found using numerical solvers at the expense of high computational complexity. We propose two algorithms (centralized and distributed) to reach a sub-optimal association at reduced complexity. Simulation results show that the performance of the proposed algorithms approaches the counterpart of its optimal solution and outperforms the state-of-the-art techniques from the literature.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".