QoS Provisioning Based Resource Allocation for Energy Harvesting Systems
Bibliographic record
Abstract
In this paper, we propose quality-of-service (QoS) based resource allocation (RA) schemes for energy harvesting (EH) systems. We consider a system model with a single source and multiple destination nodes or users, in which the source node harvests energy from the environment. Our goal is to develop efficient RA policies for EH systems when the harvested energy at the source node is uncertain and insufficient to satisfy the QoS of the users completely. We develop two different schemes and RA policies to address this problem. In the first scheme, we minimize the total dissatisfaction of the users over a finite period of time through goal programming approach. In the second scheme, we maximize the number of admitted users and provide guaranteed QoS to them. For both schemes, we first develop offline algorithms assuming the availability of perfect and complete information about the harvested energy and channel state information (CSI). Next, we devise online algorithms based on dynamic programming (DP) considering the availability of causal information about the harvested energy and CSI. Numerical results demonstrate the effectiveness of our proposed algorithms and the importance of QoS provisioning based RA schemes in EH systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".