Energy-efficient power allocation for delay-constrained systems
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Bibliographic record
Abstract
In this paper, we obtain an energy-efficient power allocation technique for a Rayleigh block-fading channel with delay-limited applications. In particular, we consider a probabilistic delay constraint as the user quality-of-service (QoS) requirement, and incorporate the concept of effective capacity to obtain the maximum arrival rate, at which, the delay constraint is satisfied. We obtain the energy efficiency (EE), which is formulated as the ratio between the effective capacity and the total expenditure power, of this system and derive the power allocation strategy that maximizes the EE. Numerical results are conducted to corroborate our theoretical results. In addition, for comparison reasons, we plot the maximum achievable EE under two well-known power allocations schemes, namely, water-filling (wf) and constant power allocation (cons) when considering delay constraints. The results show that in stringent delay limited systems, adaptive power allocation improves the maximum achievable EE significantly.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it