Enhancing Timer-Based Power Management to Support Delay-Intolerant Uplink Traffic in Infrastructure IEEE 802.11 WLANs
Bibliographic record
Abstract
Efficient power management of the radio is a critical requirement for the battery-operated portable electronic devices incorporating wireless transceivers to have a longer runtime. The power management function of IEEE 802.11 wireless local area networks (WLANs) allows stations (STAs) to operate in the doze mode to save energy significantly. In this paper, the enhanced timer-based power management (E-TPM) scheme, which supports the applications with delay-intolerant uplink traffic (DIUT), is presented for infrastructure IEEE 802.11 WLANs. With E-TPM, the radio transceiver of the dozing STA is woken up right away when an outgoing frame is generated by the STA so that DIUT is transmitted in a timely manner. In addition, a novel model for stochastic analysis of the E-TPM is developed. Based on this model, the probabilities that an STA is active, idle, or dozing are derived, and the power consumption of the STA, the number of frames buffered at the access point (AP) for the STA operating in doze mode, and the average delay per frame are obtained. These results enable an efficient power management algorithm that optimizes the idle timer and doze duration at the STA so that the doze mode does not result in extra delay in DIUT, and the delay of downlink traffic is controlled within a given bound. Numerical results show that the proposed E-TPM is able to considerably reduce delay with limited available memory space at the AP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".