Multi-Flow Carrier Aggregation in Heterogeneous Networks: Cross-Layer Performance Analysis
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Bibliographic record
Abstract
Multi-flow carrier aggregation (CA) has recently been considered to meet the increasing demand for high data rates. In this paper, we investigate the cross-layer performance of multi-flow CA for macro user equipments (MUEs) in the expanded range (ER) of small cells. We develop a fork/join (F/J) queuing analytical model that takes into account the time varying channels, the channel scheduling algorithm, partial CQI feedback and the number of component carriers deployed at each tier. Our model also accounts for stochastic packet arrivals and the packet scheduling mechanism. The analytical model developed in this paper can be used to gauge various packet-level performance parameters e.g., packet loss probability (PLP) and queuing delay. For the queuing delay, our model takes out-of-sequence packet delivery into consideration. The developed model can also be used to find the amount of CQI feedback and the packet scheduling of a particular MUE in order to offload as much traffic as possible from the macrocells to the small cells while maintaining the MUE's quality of service (QoS) requirements.
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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 | 0.000 |
| 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