Equivalent capacity analysis of LTE-Advanced systems with carrier aggregation
Bibliographic record
Abstract
The Long Term Evolution - Advanced (LTE-A) standard is widely accepted for the 4th generation mobile systems to satisfy the explosive growth of high-data-rate demand. Carrier Aggregation (CA) is considered as one of the most momentous techniques adopted in LTE-A standard. Many studies have been done to analyze the performance of LTE-A systems with CA in terms of average user throughput. However, the system-level capacity analysis of LTE-A systems has not been well studied. In this paper, we explore the downlink admission control process in LTE-A systems with CA to compare the capacities between LTE users and LTE-A users, based on the metric - equivalent capacity. Specifically, taking into account the user heterogeneity, the system evolution is modeled as a birth-death process for each user class based on an effective user traffic generation model. A closed-form relationship between the equivalent capacity and system bandwidth is then derived for a single-carrier LTE-A system with the help of binomial-normal approximation. The relationship is further extended to multi-carrier case for both LTE users and LTE-A users. Finally, simulation results are provided to verify our analytical ones, and demonstrate that the equivalent capacity of LTE-A users surpasses that of LTE users significantly.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".