Equivalent Capacity in Carrier Aggregation-Based LTE-A Systems: A Probabilistic Analysis
Bibliographic record
Abstract
In this paper, we analyze the user accommodation capabilities of LTE-A systems with carrier aggregation for the LTE users and LTE-A users, respectively. The adopted performance metric is equivalent capacity (EC), defined as the maximum number of users allowed in the system given the user QoS requirements. Specifically, both LTE and LTE-A users are divided into heterogeneous user classes with different QoS requirements, traffic characteristics and bandwidth weights. Two bandwidth allocation strategies are studied, i.e., the fixed-weight strategy and the cognitive-weight strategy, where the bandwidth weights of different user classes are prefixed under the former and dynamically changing with the cell load conditions under the latter. For each strategy, closed-form expressions of ECs of different user classes are derived for LTE and LTE-A users, respectively. A net-profit-maximization problem is further formulated to discuss the tradeoff among the bandwidth weights. Extensive simulations are conducted to corroborate our analytical results, and demonstrate an interesting discovery that only a slightly higher spectrum utilization of LTE-A users than LTE users can result in a significant EC gain when the user traffic is bursty. Moreover, the cognitive-weight strategy is shown to outperform considerably the fixed-weight one due to stronger adaptability to the cell load conditions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".