Sum Power Minimization of Greener Communications with Energy Harvesting
Bibliographic record
Abstract
Energy harvesting (EH) plays an important role in greener wireless communication systems. Causality of EH increases challenge. Guaranteeing the throughput as a quality of service (QoS), we aim at using the least power, e.g. for broadband satellite communications. Solving this target problem requires exactness and efficiency. The proposed generalized water-filling approach computes the solution to the sum power minimization problem while meet EH and QoS constraints with K epochs. The proposed algorithm with the assumption of predictable channel power gains and incoming harvested energy, possesses distinguished features: exact numerical values of the solution, i.e., the exact solution, to the target problem; and a low degree polynomial complexity, not beyond O(K2.2), which is lower than a cubic polynomial complexity in K. Since EH problems are new emergence, the conventional water fillings cannot solve the proposed problem, under the merit of exactness and less complexity. The two advantages, exactness and the efficiency, are very suitable for a real-time optimal power allocation. Really, the proposed algorithm is simple, but the reason of its optimality is profound and then omitted here. Optimality of the proposed algorithm is guaranteed. Numerical results illustrate the steps and demonstrate the efficiency of the proposed algorithm. To the best of the authors' knowledge, there is no existing reference to provide such a solution under the merit of the mentioned two advantages, including the most popular interior point method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".