Power Savings and Performance Analysis in Wireless Networks
Bibliographic record
Abstract
This paper investigates the effects of power saving strategies on the performance of wireless local area networks (WLANs). More specifically, a power management model is formulated as an integer linear program that the network planner can use in order to achieve power savings while maintaining an acceptable quality of service (QoS), measured by the signal to interference ratio (SIR) for interference limited WLAN. Furthermore, through a network simulation implemented in NS-2, it is shown that the adaptive power saving scheme can guarantee the same average throughput as the non-adaptive counterpart, while significantly reducing the total transmitted power. Considering a realistic scenario, we show that, using the proposed power management model, one can save about 55% of the transmitted power while the SIR is increased by 6 dB thus improving the QoS. Also, using a simple experiment with two access points it is shown that, in the case of users within the overlap of the two coverage areas, the throughput remains constant when the transmit power is changed from a low value to a high value although a minor degradation of the average delay is noticed. As a conclusion, the commonly assumed fact that increasing the transmit power results in better network performance is not necessarily true and can result as shown in this paper in energy waste.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".