Efficiency of Power Ramping During Random Access in LTE
Bibliographic record
Abstract
In this paper, we examine the impact of power ramping, number of retransmission attempts, and limitations of the physical downlink control channel (PDCCH) on the performance of random access in Long Term Evolution/Long Term Evolution-Advanced networks. We demonstrate that positive feedback exists between the maximum number of retransmissions, age-based power ramping, and PDCCH deficiency. As the result, system capacity is decreased and performance deteriorates abruptly under moderate to high traffic intensity. We show that, under power ramping and default PDCCH capacity, increasing the number of retransmissions beyond 1 or at most 2 does not bring any benefits and, in fact, is detrimental to system capacity. Increasing PDCCH capacity would enable the benefits of power ramping and allow more retransmission attempts. However, increasing the number of retransmissions combined with power ramping, while helpful under moderate to high load, will decrease system capacity.
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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 | 0.002 | 0.010 |
| 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 | 0.000 | 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 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".