Bibliographic record
Abstract
A Base Transceiver Station (BTS) is a piece of equipment consisting of telecommunication devices and the air interface of the mobile network. It is referred to as the BS in 3G networks, the eNB in the LTE standard, and the GNodeB for the 5G. Any wireless service provider operates a country-wide System of BTS. The System is the part of the wireless network responsible for the reception and transmission of radio signals from user equipments (UE), like mobile phones and computers with wireless internet wireless connectivity. All BTSs need to be electrically powered and system management may investigate methods to reduce power consumption. However, saving power may turn into a waste of performance (increased response time), in other words, into a waste of the BTS quality of service (QoS) This paper aim is to discuss the power management of BTS stations for the best compromise between energy-saving and response to incoming calls. The BTS management strategies that optimize the BTS power consumption (minimum absorbed Watt), the BTS performance (minimum response_time to incoming calls), and the BTS performance x Watt (minimum response_time x Watt) are identified. To compensate for the difficulties of using analytical approaches the paper uses simulation to evaluate the strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".