Performance Analysis of LTE Random Access Protocol With an Energy Harvesting M2M Scenario
Bibliographic record
Abstract
In this article, we analyze the performance of the long-term evolution random access procedure with the Third Generation Partnership Project's access class barring (ACB) mechanism in an energy harvesting (EH) machine-to-machine (M2M) scenario. To circumvent the state-space explosion in the conventional Markov-chain-based analysis due to time-dependent traffic pattern and data and energy buffer status, we develop an analytical model that combines mean-value analysis with the Markov-based analysis. Based on the analytical model, the random access success probability, the access delay of the network, and the average time duration between two successive successful transmissions are derived. Our analysis suggests that in the EH scenario, despite the lower number of the contending nodes in comparison with the non-EH scenario, the ACB parameters must be chosen in a more conservative way to avoid excessive collisions. The ACB parameters include access barring rate and mean barring duration. We also study an energy threshold-based activation policy and investigate the joint effects of this policy and the ACB mechanism on the random access success probability. The extensive simulations were conducted to evaluate the accuracy of the analytical model.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".