Measurement and Analysis of Small Cell Splitting in a Real-world LTE-A HetNet
Bibliographic record
Abstract
Network densification is an important topic which has been studied during the past decades in the 4G heterogeneous networks (HetNets). Deployment of small cells and cell-splitting technique are aimed to increase network capacity, cell coverage, and total cell throughput in HetNets. However, most published literature is about theoretical analysis. In this paper, extensive measurements are conducted in a real-world LTE-A HetNet environment. The cell-splitting strategy is applied in a real-world LTE-A HetNet. Four directional antennas operate as one cell and two cells respectively in an indoor gymnasium in the University of Regina. Optimization techniques such as ABS (Almost Blank Subframe) are utilized to mitigate interference and increase UE (user equipment) SINR inside the gymnasium. Users' (both static users and moving users) average SINR and system cell throughput are used to evaluate the performance of the tests. Our results show that operating the small cells from one cell to three cells for the whole building, the SINR inside the gymnasium decreased from 29 dB to 5 dB, and cell throughput decreased from 140 Mbps to 88Mbps. Even though the throughput performance of cells inside the gymnasium is slightly lowered, the overall network capacity of the building is enhanced.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".