Analisis Pengaruh Penggunaan AAU pada Swap RRU terhadap Kualitas Layanan Telekomunikasi di Wilayah Pusdikom Cibeureum Cimahi
Bibliographic record
Abstract
Telecommunications technology continues to undergo advancements. The enabler of communication in the field of telecommunications is the telecommunications equipment installed on a BTS tower. These devices are constantly being updated to improve the service provided by the provider to meet the needs of customers. One of the network technologies currently used is 4G LTE. To enhance the network service, one of the measures taken is performing a swap or upgrade of the telecommunications equipment on a BTS tower. The upgrade of telecommunications equipment is also influenced by the population in the vicinity of the BTS tower. This allows for the replacement of telecommunications devices with ones that have broader coverage and better signal quality. This research, we discuss the issue of network capacity shortage in the Cibeureum area, South Cimahi. This is indicated by the addition of AAUs to the BTS tower in Pusdikom, Cibeureum. This research found data that South Cimahi is the most densely populated area in the city compared to other districts. Therefore, the addition of AAUs is highly effective for this issue. It is known that the AAUs are added to replace the RRUs on the tower. This is because the capacity and transmission channels of AAUs are greater than RRUs. The addition of AAU with 32T32R specifications to the tower improves signal quality with the following parameter values: RSRP(dbm): -68, -72, -76; RSRQ(db): -11, -10, -10; and SNR(db): 1, 1, 4. These values are categorized as good, indicating that the addition of AAU to the tower can effectively increase network capacity and transmission channels, thereby improving network quality in the area.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".