Increasing Cell Throughput and Network Capacity in a Real-world HetNet Environment
Bibliographic record
Abstract
Wireless cellular networks have been extensively developed for the past decades from 3G to 5G. In this paper, a novel algorithm is proposed to increase the cell throughput in a small cell indoor environment. The algorithm combines k-means clustering and cell-splitting technique. Instead of solely using k-means clustering to cluster locations of users and small cells, an indicator is assisted to help identify potential antennas that are useful for increasing cell throughput in the optimization process. In addition, simulations and a series of data collections for verifying our algorithm are performed in a real-world LTE-A HetNet (heterogeneous network). The simulation results indicate that our algorithm can boost cell throughput of the whole system by up to 43% compared to the initial setting of directly using cell-splitting. The results of the measured data show that the algorithm can improve the cell throughput of the hotspot by about 36% in terms of basic cell-splitting settings. This algorithm will be beneficial for the network when it does not have enough capacity for users in small cell environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".