Holistic Optimization of Rate and EE in UAV-Assisted HetNets
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".