Holistic Optimization of Rate and EE in UAV-Assisted HetNets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Technological advancements are driving a surge in demand for real-time interactive applications, high-speed transmissions, and innovative network designs, necessitating enhancements in both network rate and energy efficiency (EE) to deliver immersive user experiences. This paper introduces a novel network model based unique mathematical optimization problem, employing advanced techniques such as phone user clustering (PUC)-based downlink hybrid multiple access (H-MA) within an unmanned aerial vehicle (UAV)-assisted heterogeneous network (HetNet). The objective is to concurrently improve network rate and EE by optimizing performance indicators (PIs), including phone user (PU) admission in clusters, PU association with cells, power allocation to clusters and PUs, PU fair association with cell (PUFAC), and quality of service (QoS) of PUs. The formulated optimization problem, a mixed-integer non-linear programming (MINLP) problem, is effectively addressed using an outer approximation algorithm (OAA). The paper concludes with a comprehensive assessment of the proposed PUC-based downlink H-MA technique in a UAV-assisted HetNet, considering all PIs. Additionally, it provides a performance comparison against an macro cell (MC)-only network and a HetN et, demonstrating the superior performance of the proposed technique across various metrics, including rate, EE, PU admission, PU-cell association, power allocation, PUFAC, and QoS in a UAV-assisted HetNet compared to both the MC-only network and HetNet.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it