Power Allocation for Cognitive Energy Harvesting and Smart Power Grid Coexisting System
Bibliographic record
Abstract
Cognitive radio (CR) lifts efficiency of information resource. As one way of utilizing the renewable energy resources, energy harvesting makes use of energy from the environment. Due to intermitted feature of the renewable energy, the power grid needs to be integrated to regulate the harvested energy supply of the system. Thus, the transmit power of the second user (SU), including the power from both the renewable energy and the power grid is often subject to a peak power constraint to control the interference level of the SU to the primary user (PU). The combination of these three types of emerging communication machineries renders great challenge to provide exact optimal power allocation solution with rapid computation. To the best knowledge of the authors, no such kind of solutions were reported in the open literature. In this paper, our recently proposed geometric water-filling with peak power constraints (GWFPP) and recursion machinery are applied and exploited to solve the throughput maximization problems, making the power grid smart. The proposed algorithms are precisely defined. They provide the exact optimal solution with efficient finite computation. Their optimality is strictly proved. Numerical examples and computational complexity analysis are presented to illustrate the procedures and demonstrate the efficiency of the proposed algorithms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".