Cross-layer carrier selection and power control for LTE-A uplink with Carrier Aggregation
Bibliographic record
Abstract
Long Term Evolution-Advanced (LTE-A) standard with Carrier Aggregation (CA) is emerging as a promising technology for 4G mobile communication systems to fulfill tremendous growth of high-data-rate demand. However, in LTE-A systems with CA, the uplink Radio Resource Management (RRM) performance is greatly limited by the insufficient user transmission power and the infamous power offset effects. In this paper, we design a cross-layer carrier selection and power control strategy for LTE-A uplink with CA to improve the average user throughput, while dealing with the above limitations. Specifically, we first propose a novel estimation method to effectively predict the average bandwidth that a newly admitted user can obtain from each carrier. The time-variability of carrier load conditions is carefully taken into account. Then, an optimal carrier subset and power allocation values are determined for each arrived user to improve the average user throughput by solving a user-power-utilization maximization problem, with considering the user power constraints and offset effects. Extensive simulations validate the effectiveness of the estimation method and demonstrate that the proposed cross-layer strategy can achieve higher average user throughput compared with the existing approach.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".