Optimizing Cell Association and Stability in Integrated Aerial-to-Ground Next-Generation Consumer Wireless Networks
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Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) offer advantages in serving as aerial small cells (ASCs) to support public safety terrestrial cells (PSTCs) while providing pervasive coverage during disasters. To ensure reliable communications for long-term evolution-based public safety (PS-LTE) users, it is crucial to obtain an accurate understanding of network performance for practical cell association design and network stability. This comprehension is vital for the practical design of cell associations and for maintaining network stability in next-generation consumer wireless networks. For this purpose, we first employ a flexible biased cell association (FBCA) policy that optimally selects the bias factor where a PS-LTE user (PUE) connects to the eNodeB (eNB) giving the maximum power for the received signal. Then, we present a resource allocation and subframe-type selection by formulating stochastic optimization programming to resolve system stability issues in the coexisting PS-LTE andLTE-based high-speed railway (LTE-R) networks and PS-LTE and UAV networks. In addition to this, we employ the Lyapunov optimization technique to seek an optimal almost blank subframe (ABS) algorithm with dynamic delay-aware resource allocation (ADDRA) to resolve the problem of network stability. Using ADDRA, the PS-LTE eNodeB (PSeNB), the aerial eNodeBs (AeNBs), and the LTE-R eNodeBs (ReNBs) obtain up-to-date queues of attached users and accordingly compute a matrix for scheduling resources based on channel state information (CSI) feedback. The simulation results of the UAV-assisted networks using FBCA and ADDRA in coexisting PS-LTE/LTE-R and PS-LTE/UAV networks demonstrate a significant improvement when compared with other state-of-the-art techniques.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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