Cross-Layer Performance Analysis of Downlink Multi-Flow Carrier Aggregation in Heterogeneous Networks
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Bibliographic record
Abstract
Multi-flow carrier aggregation (CA) is an emerging technique that is implemented to improve the capacity of cellular networks. In this paper, we study the cross-layer performance of user equipments (UEs) in heterogeneous networks under multi-flow CA. We develop a queuing analytical model for measuring packet-level performance parameters, e.g., packet loss probability and queuing delay. Our developed model accounts for the time-varying channels, the channel scheduling algorithm, partial channel quality information feedback, and the number of component carriers deployed at each tier. Our model also takes into consideration stochastic packet arrivals, the packet scheduling algorithm, and out-of-sequence packet delivery. The developed model can be used to tune the various system and operating parameters in order to offload traffic from the macrocells to the small cells while maintaining the quality of service requirements of UEs. The accuracy of the analytical model developed in this paper is validated through computer simulations.
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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 | 0.001 |
| 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